Arrangements for detecting the presence or location of an object in a vehicle and for controlling deployment of a safety restraint

ABSTRACT

Arrangements for a vehicle including ultrasonic transmitters for transmitting ultrasonic waves into the passenger compartment, each transmitter operating at a distinct transmitting frequency and positioned at a distinct location relative to the other transmitter(s), at least one receiver disposed so as to receive ultrasonic waves transmitted from a transmitter and modified by passing through the passenger compartment, and a processor operatively coupled to the receiver(s). Based on the received waves, the processor determines whether an object is located in the passenger compartment, controls deployment of a safety restraint device and/or determines the location of an object in the passenger compartment. Other arrangements include receivers arranged at different locations in the vehicle and which facilitate a distance measurement capability. A processor analyzes the distances between the object and each receiver and based thereon, determines if an object is present, controls a safety restraint device and/or determines the location of an object.

CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority under 35 U.S.C. §119(e) of U.S.provisional patent application Ser. No. 60/136,163 filed May 27, 1999through U.S. patent application Ser. No. 09/474,147 filed Dec. 19, 1999.

[0002] This application is continuation of U.S. patent application Ser.No. 09/474,147 filed Dec. 29, 1999 which is a continuation-in-part ofU.S. patent application Ser. No. 09/382,406 filed Aug. 4, 1999 which isa continuation-in-part of U.S. patent application Ser. No. 08/919,823,now U.S. Pat. No. 5,943,295, which is a continuation-in-part of U.S.patent application Ser. No. 08/798,029 filed Feb. 6, 1997, nowabandoned, which claims priority of U.S. provisional patent applicationSer. No. 60/011,351 filed Feb. 8, 1996.

[0003] This application also claims priority under 35 U.S.C. §119(e) ofU.S. provisional patent application Ser. No. 60/011,351 through theparent applications.

[0004] This application is related to: (i) U.S. Pat. No. 5,653,462entitled “Vehicle Occupant Position and Velocity Sensor” filed Jul. 21,1995, which is a continuation of U.S. patent application Ser. No.08/040,978 filed Mar. 31, 1993, now abandoned, which in turn is acontinuation of U.S. patent application Ser. No. 07/878,571 filed May 5,1992, now abandoned; (ii) U.S. Pat. No. 5,829,782 entitled “VehicleInterior Identification and Monitoring System” filed May 9, 1994; (iii)U.S. Pat. No. 5,845,000 entitled “Optical Identification and MonitoringSystem Using Pattern Recognition for Use with Vehicles” filed Jun. 7,1995; (iv) U.S. Pat. No. 5,822,707 entitled “Automatic Vehicle SeatAdjuster” filed Jun. 7, 1995; (v) U.S. Pat. No. 5,748,473 entitled“Automatic Vehicle Seat Adjuster” filed Jun. 7, 1995; and, (vi) U.S.Pat. No. 5,835,613 entitled “Optical Identification and MonitoringSystem Using Pattern Recognition for Use with Vehicles” filed Jun. 7,1995, which are all incorporated by reference herein.

FIELD OF THE INVENTION

[0005] The present invention relates generally to the fields of sensing,detecting, monitoring and/or identifying various objects, and partsthereof, which are located within the passenger compartment of a motorvehicle. The invention also relates to methods for controllingdeployment of an occupant restraint in a vehicle.

[0006] Further, the present invention relates to an efficient and highlyreliable method for developing a system for detecting the orientation ofan object in the passenger compartment, e.g., a rear facing child seat(RFCS) situated in the passenger compartment in a location where it mayinteract with a deploying occupant protection apparatus, such as anairbag, and/or for detecting an out-of-position occupant. The resultingsystem permits the control and selective suppression of deployment ofthe occupant protection apparatus when the deployment may result ingreater injury to the occupant than the crash forces themselves. This isaccomplished in part through a method of determining the placement oftransducers of the system, a method of developing a pattern recognitionsystem including a method of training a neural network and/or a methodfor developing a system for the novel analysis of the signals from thetransducers.

[0007] The application of the occupant position sensor to a newautomobile vehicle model is called applications engineering.Applications engineering of occupant sensors comprises, inter alia,determining the location of the transducers, designing the transducerholders, determining the wiring layout, performing a tolerance study onthe transducer locations and angular orientation, designing the circuitsfor the particular vehicle model, interfacing or integrating thecircuits into the vehicle electronic system, and adapting the occupantsensor system to the particular vehicle model.

[0008] All of the above aspects of application engineering, with theexception of the system adaptation, are standard processes that do notdiffer significantly from the application engineering of any electronicsystem to a new vehicle model. The system adaptation, however, is uniquein that it requires considerable skill and expertise and the use ofnovel technologies to create a system that is optimized for a particularvehicle.

BACKGROUND OF THE INVENTION

[0009] 1. Prior Art on Sensing of Out-Of-Position Occupants and RearFacing Child Seats

[0010] Whereas thousands of lives have been saved by airbags, a largenumber of people have also been injured, some seriously, by thedeploying airbag, and thus significant improvements to the airbag systemare necessary. As discussed in detail in one or more of the patents andpatent applications cross-referenced above, for a variety of reasons,vehicle occupants may be too close to the airbag before it deploys andcan be seriously injured or killed as a result of any deploymentthereof. Also, a child in a rear facing child seat which is placed onthe right front passenger seat is in danger of being seriously injuredif the passenger airbag deploys. For these reasons and, as firstpublicly disclosed in Breed, D. S. “How Airbags Work” presented at theInternational Conference on Seatbelts and Airbags in 1993, in Canada,occupant position sensing and rear facing child seat detection isrequired in order to minimize the damages caused by deploying airbags.It is also be required in order to minimize the damage caused by thedeployment of other types of occupant protection and/or restraintdevices which might be installed in the vehicle.

[0011] Initially, these systems will solve the out-of-position occupantand the rear facing child seat problems related to current airbagsystems and prevent unneeded and unwanted airbag deployments when afront seat is unoccupied. However, airbags are now under development toprotect rear seat occupants in vehicle crashes and all occupants in sideimpacts. A system is therefore needed to detect the presence ofoccupants, determine if they are out-of-position (defined below) and toidentify the presence of a rear facing child seat in the rear seat.Future automobiles are expected to have eight or more airbags asprotection is sought for rear seat occupants and from side impacts. Inaddition to eliminating the disturbance and possible harm of unnecessaryairbag deployments, the cost of replacing these airbags will beexcessive if they all deploy in an accident needlessly.

[0012] Inflators now exist which will adjust the amount of gas flowingto or from the airbag to account for the size and position of theoccupant and for the severity of the accident. The vehicleidentification and monitoring system (VIMS) discussed in U.S. Pat. No.5,829,782, and U.S. patent application Ser. No. 08/798,029 filed Feb. 6,1997 among others, will control such inflators based on the presence andposition of vehicle occupants or of a rear facing child seat. Theinstant invention is concerned with the process of adapting the vehicleinterior monitoring systems to a particular vehicle model and achievinga high system accuracy and reliability as discussed in greater detailbelow.

[0013] The automatic adjustment of the deployment rate of the airbagbased on occupant identification and position and on crash severity hasbeen termed “smart airbags”. Central to the development of smart airbagsis the occupant identification and position determination systemsdescribed in the above-referenced patents and patent applications and tothe methods described herein for adapting those systems to a particularvehicle model. To complete the development of smart airbags, ananticipatory crash detecting system such as disclosed in U.S. patentapplication Ser. No. 08/247,760 filed May 23, 1994 is also desirable.Prior to the implementation of anticipatory crash sensing, the use of aneural network smart crash sensor which identifies the type of crash andthus its severity based on the early part of the crash accelerationsignature should be developed and thereafter implemented. U.S. Pat. No.5,684,701 (Breed) describes a crash sensor based on neural networks.This crash sensor, as with all other crash sensors, determines whetheror not the crash is of sufficient severity to require deployment of theairbag and, if so, initiates the deployment. A neural network based on asmart airbag crash sensor could also be designed to identify the crashand categorize it with regard to severity thus permitting the airbagdeployment to be matched not only to the characteristics and position ofthe occupant but also the severity and timing of the crash itself (thisbeing described in U.S. patent application Ser. No. 08/798,029referenced above).

[0014] The need for an occupant out-of-position sensor has also beenobserved by others and several methods have been described in certainU.S. patents for determining the position of an occupant of a motorvehicle. However, no patents have been found that describe the methodsof adapting such sensors to a particular vehicle model to obtain highsystem accuracy. Each of these systems will be discussed below and havesignificant limitations.

[0015] In White et al. (U.S. Pat. No. 5,071,160), for example, a singleacoustic sensor and detector is described and, as illustrated, ismounted lower than the steering wheel. White et al. correctly perceivethat such a sensor could be defeated, and the airbag falsely deployed,by an occupant adjusting the control knobs on the radio and thus theysuggest the use of a plurality of such sensors. White et al. does notdisclose where the such sensors would be mounted, other than on theinstrument panel below the steering wheel, or how they would be combinedto uniquely monitor particular locations in the passenger compartmentand to identify the object(s) occupying those locations. The adaptationprocess to vehicles is not described.

[0016] Mattes et al. (U.S. Pat. No. 5,118,134) describe a variety ofmethods for measuring the change in position of an occupant includingultrasonic, active or passive infrared radiation and microwave radarsensors, and an electric eye. The use of these sensors is to measure thechange in position of an occupant during a crash and they use thatinformation to assess the severity of the crash and thereby decidewhether or not to deploy the airbag. They are thus using the occupantmotion as a crash sensor. No mention is made of determining theout-of-position status of the occupant or of any of the other featuresof occupant monitoring as disclosed in the above-referenced patentsand/or patent applications. It is interesting to note that nowhere doesMattes et al. discuss how to use a combination of ultrasonicsensors/transmitters to identify the presence of a human occupant andthen to find his/her location in the passenger compartment.

[0017] The object of an occupant out-of-position sensor is to determinethe location of, e.g., the head and/or chest of the vehicle occupant inthe passenger compartment to enable the location of the head and/orchest to be determined relative to the occupant protection apparatus,e.g., airbag, since it is the impact of either the head or chest withthe deploying airbag which can result in serious injuries. Both White etal. and Mattes et al. disclose only lower mounting locations of theirsensors which are mounted in front of the occupant such as on thedashboard or below the steering wheel. Both such mounting locations areparticularly prone to detection errors due to positioning of theoccupant's hands, arms and legs. This would require at least three, andpreferably more, such sensors and detectors and an appropriate logiccircuitry which ignores readings from some sensors if such readings areinconsistent with others, for the case, for example, where the driver'sarms are the closest objects to two of the sensors. The determination ofthe proper transducer mounting locations, aiming and field angles for aparticular vehicle model are not disclosed in either White et al. orMattes et al. and are part of the vehicle model adaptation processdescribed herein.

[0018] White et al. also describe the use of error correction circuitry,without defining or illustrating the circuitry, to differentiate betweenthe velocity of one of the occupant's hands, as in the case where he/sheis adjusting the knob on the radio, and the remainder of the occupant.Three ultrasonic sensors of the type disclosed by White et al. might, insome cases, accomplish this differentiation if two of them indicatedthat the occupant was not moving while the third was indicating that heor she was moving. Such a combination, however, would not differentiatebetween an occupant with both hands and arms in the path of theultrasonic transmitter at such a location that they were blocking asubstantial view of the occupant's head or chest. Since the sizes anddriving positions of occupants are extremely varied, trained patternrecognition systems, such as neural networks, are required when a clearview of the occupant, unimpeded by his/her extremities, cannot beguaranteed. White et al. do not suggest the use of such neural networks.

[0019] Fujita et al., in U.S. Pat. No. 5,074,583, describe anothermethod of determining the position of the occupant but do not use thisinformation to control and suppress deployment of an airbag if theoccupant is out-of-position, or if a rear facing child seat is present.In fact, the closer that the occupant gets to the airbag, the faster theinflation rate of the airbag is according to the Fujita et al. patent,which thereby increases the possibility of injuring the occupant. Fujitaet al. do not measure the occupant directly but instead determine his orher position indirectly from measurements of the seat position and thevertical size of the occupant relative to the seat. This occupant heightis determined using an ultrasonic displacement sensor mounted directlyabove the occupant's head.

[0020] It is important to note that in all cases in the above-citedprior art, except those assigned to the current assignee of the instantinvention, no mention is made of the method of determining transducerlocation, deriving the algorithms or other system parameters that allowthe system to accurately identify and locate an object in the vehicle.In contrast, in one implementation of the instant invention, the returnultrasonic echo pattern over several milliseconds corresponding to theentire portion of the passenger compartment volume of interest isanalyzed from multiple transducers and sometimes combined with theoutput from other transducers, providing distance information to manypoints on the items occupying the passenger compartment.

[0021] Many of the teachings of this invention are based on patternrecognition technologies as taught in numerous textbooks and technicalpapers. Central to the diagnostic teachings of this invention is themanner in which the diagnostic module determines a normal pattern froman abnormal pattern and the manner in which it decides what data to usefrom the vast amount of data available. This is accomplished usingpattern recognition technologies, such as artificial neural networks,and training. The theory of neural networks including many examples canbe found in several books on the subject including: Techniques AndApplication Of Neural Networks, edited by Taylor, M. and Lisboa, P.,Ellis Horwood, West Sussex, England, 1993; Naturally IntelligentSystems, by Caudill, M. and Butler, C., MIT Press, Cambridge Mass.,1990; J. M. Zaruda, Introduction to Artificial Neural Systems, Westpublishing Co., N.Y., 1992 and, Digital Neural Networks, by Kung, S. Y.,PTR Prentice Hall, Englewood Cliffs, N.J., 1993, Eberhart, R., Simpson,P. and Dobbins, R., Computational Intelligence PC Tools, Academic Press,Inc., 1996, Orlando, Fla., all of which are included herein byreference. The neural network pattern recognition technology is one ofthe most developed of pattern recognition technologies.

[0022] 2. Definitions

[0023] The use of pattern recognition, or more particularly how it isused, is central to the instant invention. In the above-cited prior art,except in that assigned to the current assignee of the instantinvention, pattern recognition which is based on training, asexemplified through the use of neural networks, is not mentioned for usein monitoring the interior passenger compartment or exteriorenvironments of the vehicle. Thus, the methods used to adapt suchsystems to a vehicle are also not mentioned.

[0024] “Pattern recognition” as used herein will generally mean anysystem which processes a signal that is generated by an object (e.g.,representative of a pattern of returned or received impulses, waves orother physical property specific to and/or characteristic of and/orrepresentative of that object) or is modified by interacting with anobject, in order to determine to which one of a set of classes that theobject belongs. Such a system might determine only that the object is oris not a member of one specified class, or it might attempt to assignthe object to one of a larger set of specified classes, or find that itis not a member of any of the classes in the set. The signals processedare generally a series of electrical signals coming from transducersthat are sensitive to acoustic (ultrasonic) or electromagnetic radiation(e.g., visible light or infrared radiation), although other sources ofinformation are frequently included.

[0025] A trainable or a trained pattern recognition system as usedherein generally means a pattern recognition system which is taught torecognize various patterns constituted within the signals by subjectingthe system to a variety of examples. The most successful such system isthe neural network. Thus, to generate the pattern recognition algorithm,test data is first obtained which constitutes a plurality of sets ofreturned waves, or wave patterns, from an object (or from the space inwhich the object will be situated in the passenger compartment, i.e.,the space above the seat) and an indication of the identify of thatobject, (e.g., a number of different objects are tested to obtain theunique wave patterns from each object). As such, the algorithm isgenerated, and stored in a computer processor, and which can later beapplied to provide the identity of an object based on the wave patternbeing received during use by a receiver connected to the processor andother information. For the purposes here, the identity of an objectsometimes applies to not only the object itself but also to its locationand/or orientation in the passenger compartment. For example, a rearfacing child seat is a different object than a forward facing child seatand an out-of-position adult is a different object than a normallyseated adult.

[0026] To “identify” as used herein will generally mean to determinethat the object belongs to a particular set or class. The class may beone containing, for example, all rear facing child seats, one containingall human occupants, or all human occupants not sitting in a rear facingchild seat depending on the purpose of the system. In the case where aparticular person is to be recognized, the set or class will containonly a single element, i.e., the person to be recognized.

[0027] An “occupying item” of a seat may be a living occupant such as ahuman or a dog, another living organism such as a plant, or an inanimateobject such as a box or bag of groceries or an empty child seat.

[0028] “Out-of-position” as used for an occupant will generally meansthat the occupant, either the driver or a passenger, is sufficientlyclose to the occupant protection apparatus (airbag) prior to deploymentthat he or she is likely to be more seriously injured by the deploymentevent itself than by the accident. It may also mean that the occupant isnot positioned appropriately in order to attain beneficial, restrainingeffects of the deployment of the airbag. As for the occupant being tooclose to the airbag, this typically occurs when the occupant's head orchest is closer than some distance such as about 5 inches from thedeployment door of the airbag module. The actual distance value whereairbag deployment should be suppressed depends on the design of theairbag module and is typically farther for the passenger airbag than forthe driver airbag.

[0029] “Transducer” as used herein will generally mean the combinationof a transmitter and a receiver. In come cases, the same device willserve both as the transmitter and receiver while in others two separatedevices adjacent to each other will be used. In some cases, atransmitter is not used and in such cases transducer will mean only areceiver. Transducers include, for example, capacitive, inductive,ultrasonic, electromagnetic (antenna, CCD, CMOS arrays), weightmeasuring or sensing devices.

[0030] “Adaptation” as used here represents the method by which aparticular occupant sensing system is designed and arranged for aparticular vehicle model. It includes such things as the process bywhich the number, kind and location of various transducers isdetermined. For pattern recognition systems, it includes the process bywhich the pattern recognition system is taught to recognize the desiredpatterns. In this connection, it will usually include (1) the method oftraining, (2) the makeup of the databases used for training, testing andvalidating the particular system, or, in the case of a neural network,the particular network architecture chosen, (3) the process by whichenvironmental influences are incorporated into the system, and (4) anyprocess for determining the pre-processing of the data or the postprocessing of the results of the pattern recognition system. The abovelist is illustrative and not exhaustive. Basically, adaptation includesall of the steps that are undertaken to adapt transducers and othersources of information to a particular vehicle to create the systemwhich accurately identifies and determines the location of an occupantor other object in a vehicle.

[0031] In the description herein on anticipatory sensing, the term“approaching” when used in connection with the mention of an object orvehicle approaching another will generally mean the relative motion ofthe object toward the vehicle having the anticipatory sensor system.Thus, in a side impact with a tree, the tree will be considered asapproaching the side of the vehicle and impacting the vehicle. In otherwords, the coordinate system used in general will be a coordinate systemresiding in the target vehicle. The “target” vehicle is the vehiclewhich is being impacted. This convention permits a general descriptionto cover all of the cases such as where (i) a moving vehicle impactsinto the side of a stationary vehicle, (ii) where both vehicles aremoving when they impact, or (iii) where a vehicle is moving sidewaysinto a stationary vehicle, tree or wall.

[0032] 3. Pattern Recognition Prior Art

[0033] Japanese Patent 3-42337 (A) to Ueno describes a device fordetecting the driving condition of a vehicle driver comprising a lightemitter for irradiating the face of the driver and a means for pickingup the image of the driver and storing it for later analysis. Means areprovided for locating the eyes of the driver and then the irises of theeyes and then determining if the driver is looking to the side orsleeping. Ueno determines the state of the eyes of the occupant ratherthan determining the location of the eyes relative to the other parts ofthe vehicle passenger compartment. Such a system can be defeated if thedriver is wearing glasses, particularly sunglasses, or another opticaldevice which obstructs a clear view of his/her eyes. Pattern recognitiontechnologies such as neural networks are not used. The method of findingthe eyes is described but not a method of adapting the system to aparticular vehicle model.

[0034] U.S. Pat. No. 5,008,946 to Ando uses a complicated set of rulesto isolate the eyes and mouth of a driver and uses this information topermit the driver to control the radio, for example, or other systemswithin the vehicle by moving his eyes and/or mouth. Ando uses naturallight and illuminates only the head of the driver. He also makes no useof trainable pattern recognition systems such as neural networks, nor isthere any attempt to identify the contents of the vehicle nor of theirlocation relative to the vehicle passenger compartment. Rather, Ando islimited to control of vehicle devices by responding to motion of thedriver's mouth and eyes. As with Ueno, a method of finding the eyes isdescribed but not a method of adapting the system to a particularvehicle model.

[0035] U.S. Pat. No. 5,298,732 to Chen also concentrates in locating theeyes of the driver so as to position a light filter between a lightsource such as the sun or the lights of an oncoming vehicle, and thedriver's eyes. Chen does not explain in detail how the eyes are locatedbut does supply a calibration system whereby the driver can adjust thefilter so that it is at the proper position relative to his or her eyes.Chen references the use of an automatic equipment for determining thelocation of the eyes but does not describe how this equipment works. Inany event, in Chen, there is no mention of monitoring the position ofthe occupant, other that the eyes, determining the position of the eyesrelative to the passenger compartment, or identifying any other objectin the vehicle other than the driver's eyes. Also, there is no mentionof the use of a trainable pattern recognition system. A method forfinding the eyes is described but not a method of adapting the system toa particular vehicle model.

[0036] U.S. Pat. No. 5,305,012 to Faris also describes a system forreducing the glare from the headlights of an oncoming vehicle. Farislocates the eyes of the occupant by using two spaced apart infraredcameras using passive infrared radiation from the eyes of the driver.Faris is only interested in locating the driver's eyes relative to thesun or oncoming headlights and does not identify or monitor the occupantor locate the occupant, a rear facing child seat or any other object forthat matter, relative to the passenger compartment or the airbag. Also,Faris does not use trainable pattern recognition techniques such asneural networks. Faris, in fact, does not even say how the eyes of theoccupant are located but refers the reader to a book entitled RobotVision (1991) by Berthold Horn, published by MIT Press, Cambridge, Mass.Also, Faris uses the passive infrared radiation rather than illuminatingthe occupant with ultrasonic or electromagnetic radiation as in someimplementations of the instant invention. A method for finding the eyesof the occupant is described but not a method of adapting the system toa particular vehicle model.

[0037] The use of neural networks as the pattern recognition technologyand the methods of adapting this to a particular vehicle, such as thetraining methods, is important to this invention since it makes themonitoring system robust, reliable and accurate. The resulting algorithmcreated by the neural network program is usually only a few hundredlines of code written in the C computer language and is in general fewerlines than when the techniques of the above patents to Ando, Chen andFaris are implemented. As a result, the resulting systems are easy toimplement at a low cost making them practical for automotiveapplications. The cost of the ultrasonic transducers, for example, isexpected to be less than about $1 in quantities of one million per year.Similarly, the implementation of the techniques of the above-referencedpatents requires expensive microprocessors while the implementation withneural networks and similar trainable pattern recognition technologiespermits the use of low cost microprocessors typically costing less thanabout $5 in quantities of one million per year.

[0038] The present invention uses sophisticated software that developstrainable pattern recognition algorithms such as neural networks.Usually the data is preprocessed, as discussed below, using variousfeature extraction techniques and the results post-processed to improvesystem accuracy. A non-automotive example of such a pattern recognitionsystem using neural networks on sonar signals is discussed in two papersby Gorman, R. P. and Sejnowski, T. J. “Analysis of Hidden Units in aLayered Network Trained to Classify Sonar Targets”, Neural Networks,Vol. 1. pp. 75-89, 1988, and “Learned Classification of Sonar TargetsUsing a Massively Parallel Network”, IEEE Transactions on Acoustics,Speech, and Signal Processing, Vol. 36, No. 7, July 1988. Examples offeature extraction techniques can be found in U.S. Pat. No. 4,906,940entitled “Process and Apparatus for the Automatic Detection andExtraction of Features in Images and Displays” to Green et al. Examplesof other more advanced and efficient pattern recognition techniques canbe found in U.S. Pat. No. 5,390,136 entitled “Artificial Neuron andMethod of Using Same and U.S. patent application Ser. No. 08/076,601entitled “Neural Network and Method of Using Same” to Wang, S. T. Otherexamples include U.S. Pat. No. 5,235,339 (Morrison et al.), U.S. Pat.No. 5,214,744 (Schweizer et al), U.S. Pat. No. 5,181,254 (Schweizer etal), and U.S. Pat. No. 4,881,270 (Knecht et al). All of the referencesherein are included herein by reference.

[0039] 4. Ultrasonics and Optics

[0040] Both laser and non-laser optical systems in general are good atdetermining the location of objects within the two dimensional plane ofthe image and a pulsed laser radar system in the scanning mode candetermine the distance of each part of the image from the receiver bymeasuring the time of flight through range gating techniques. It is alsopossible to determine distance with the non-laser system by focusing, orstereographically if two spaced apart receivers are used and, in somecases, the mere location in the field of view can be used to estimatethe position relative to the airbag, for example. Finally, a recentlydeveloped pulsed quantum well diode laser also provides inexpensivedistance measurements as discussed in U.S. provisional patentapplication Ser. No. 60/114,507, filed Dec. 31, 1998, which is includedherein by reference as if the entire contents were copied here.

[0041] Acoustic systems are additionally quite effective at distancemeasurements since the relatively low speed of sound permits simpleelectronic circuits to be designed and minimal microprocessor capabilityis required. If a coordinate system is used where the z axis is from thetransducer to the occupant, acoustics are good at measuring z dimensionswhile simple optical systems using a single CCD or CMOS arrays are goodat measuring x and y dimensions. The combination of acoustics andoptics, therefore, permits all three measurements to be made from onelocation with low cost components as discussed in commonly assigned U.S.Pat. Nos. 5,845,000 and 5,835,613 cross-referenced above.

[0042] One example of a system using these ideas is an optical systemwhich floods the passenger seat with infrared light coupled with a lensand a receiver array, e.g., CCD or CMOS array, which receives anddisplays the reflected light and an analog to digital converter (ADC)which digitizes the output of the CCD or CMOS and feeds it to anArtificial Neural Network (ANN) or other pattern recognition system foranalysis. This system uses an ultrasonic transmitter and receiver formeasuring the distances to the objects located in the passenger seat.The receiving transducer feeds its data into an ADC and from there theconverted data is directed into the ANN. The same ANN can be used forboth systems thereby providing full three-dimensional data for the ANNto analyze. This system, using low cost components, will permit accurateidentification and distance measurements not possible by either systemacting alone. If a phased array system is added to the acoustic part ofthe system, the optical part can determine the location of the driver'sears, for example, and the phased array can direct a narrow beam to thelocation and determine the distance to the occupant's ears.

[0043] Although the use of ultrasound for distance measurement has manyadvantages, it also has some drawbacks. First, the speed of sound limitsthe rate at which the position of the occupant can be updated toapproximately 10 milliseconds, which though sufficient for most cases,is marginal if the position of the occupant is to be tracked during avehicle crash. Second, ultrasound waves are diffracted by changes in airdensity that can occur when the heater or air conditioner is operated orwhen there is a high-speed flow of air past the transducer. Third, theresolution of ultrasound is limited by its wavelength and by thetransducers, which are high Q tuned devices. Typically, the resolutionof ultrasound is on the order of about 2 to 3 inches. Finally, thefields from ultrasonic transducers are difficult to control so thatreflections from unwanted objects or surfaces add noise to the data.

[0044] Ultrasonics alone can be used in several configurations formonitoring the interior of a passenger compartment of an automobile asdescribed in the above-referenced patents and patent applications and inparticular in U.S. patent application Ser. No. 08/798,029. Using theteachings of this invention, the optimum number and location of theultrasonic and/or optical transducers can be determined as part of theadaptation process for a particular vehicle model.

[0045] In the cases of the instant invention, as discussed in moredetail below, regardless of the number of transducers used, a trainedpattern recognition system, as defined above, is used to identify andclassify, and in some cases to locate, the illuminated object and itsconstituent parts.

[0046] 5. Applications

[0047] The applications for this technology are numerous as described inthe patents and patent applications listed above. However, the mainfocus of the instant invention is the process of adapting the system inthe patents and patent applications referenced above for the detectionof the presence of an occupied child seat in the rear facing position oran out-of-position occupant and the detection of an occupant in a normalseating position. The system is designed so that in the former twocases, deployment of the occupant protection apparatus (airbag) may becontrolled and possibly suppressed and in the latter, it will becontrolled and enabled.

[0048] One preferred implementation of a first generation occupantsensing system, which is adapted to various vehicle models using theteachings presented herein, is an ultrasonic occupant position sensor.This system uses an Artificial Neural Network (ANN) to recognizepatterns that it has been trained to identify as either airbag enable orairbag disable conditions. The pattern is obtained from four ultrasonictransducers that cover the front passenger seating area. This patternconsists of the ultrasonic echoes bouncing off of the objects in thepassenger seat area. The signal from each of the four transducersconsists of the electrical image of the return echoes, which isprocessed by the electronics. The electronic processing comprisesamplification, logarithmic compression, rectification, and demodulation(band pass filtering), followed by discretization (sampling) anddigitization of the signal. The only software processing required,before this signal can be fed into the artificial neural network, isnormalization (i.e., mapping the input to numbers between 0 and 1).Although this is a fair amount of processing, the resulting signal isstill considered “raw”, because all information is treated equally.

OBJECTS AND SUMMARY OF THE INVENTION

[0049] In general, it is an object of the present invention to providenew and improved arrangements for detecting the presence of an object ina passenger compartment of a vehicle.

[0050] It is another object of the present invention to provide a newand improved method for developing a system for identifying thepresence, position and orientation of an object in a vehicle.

[0051] It is another broad object of the present invention to provide amethod for developing a system for accurately detecting the presence ofan occupied rear-facing child seat in order to prevent an occupantprotection apparatus such as an airbag from deploying, when the airbagwould impact against the rear-facing child seat if deployed.

[0052] It is yet another broad object of the present invention toprovide a method for developing a system for accurately detecting thepresence of an out-of-position occupant in order to prevent one or moredeployable occupant protection apparatus such as airbags from deployingwhen the airbag(s) would impact against the head or chest of theoccupant during its initial deployment phase causing injury or possibledeath to the occupant.

[0053] The invention is also a method to develop and adapt a system toidentify, locate and monitor occupants, including their parts, and otherobjects in the passenger compartment and in particular an occupied childseat in the rear facing position or an out-of-position occupant, byilluminating the contents of the vehicle with ultrasonic orelectromagnetic radiation, for example, by transmitting radiation wavesfrom a wave generating apparatus into a space above the seat, andreceiving radiation modified by passing through at least part of thespace above the seat using two or more transducers properly located inthe vehicle passenger compartment, in specific predetermined optimumlocations. More particularly, this invention relates to a method forappropriately locating and mounting the transducers and for analyzingthe received radiation from any object which modifies the waves, inorder to achieve an accuracy of recognition heretofore not possible.Outputs from the receivers, are analyzed by appropriate computationalmeans employing trained pattern recognition technologies, to classify,identify and/or locate the contents, and/or determine the orientationof, for example, a rear facing child seat. In general, the informationobtained by the identification and monitoring system is used to affectthe operation of some other system, component or device in the vehicleand particularly the passenger and/or driver airbag systems, which mayinclude a front airbag, a side airbag, a knee bolster, or combinationsof the same. However, the information obtained can be used for amultitude of other vehicle systems.

[0054] When the vehicle interior monitoring system developed using theteachings of this invention is installed in the passenger compartment ofan automotive vehicle equipped with a occupant protection apparatus,such as an inflatable airbag, and the vehicle is subjected to a crash ofsufficient severity that the crash sensor has determined that theprotection apparatus is to be deployed, the system, developed inaccordance with the invention, has determined prior to the deploymentwhether a child placed in the rear facing position in the child seat ispresent and if so, a signal has been sent to the control circuitry thatthe airbag should be controlled and most likely disabled and notdeployed in the crash. It must be understood though that instead ofsuppressing deployment, it is possible that the deployment may becontrolled so that it might provide some meaningful protection for theoccupied rear-facing child seat. The system developed using theteachings of this invention also determines the position of the vehicleoccupant relative to the airbag and controls and possibly disablesdeployment of the airbag if the occupant is positioned so that he/she islikely to be injured by the deployment of the airbag. As before, thedeployment is not necessarily disabled but may be controlled to provideprotection for the out-of-position occupant.

[0055] Principle objects and advantages of the methods in accordancewith the invention are:

[0056] 1. To provide a reliable method for developing and adapting asystem for recognizing the presence and orientation of a child seat on aparticular seat of a motor vehicle.

[0057] 2. To provide a reliable method for developing and adapting asystem for recognizing the presence of a human being on a particularseat of a motor vehicle.

[0058] 3. To provide a reliable method for developing and adapting asystem for determining the position, velocity or size of an occupant ina motor vehicle.

[0059] 4. To provide a reliable method for developing and adapting asystem for determining in a timely manner that an occupant isout-of-position, or will become out-of-position, and likely to beinjured by a deploying airbag.

[0060] 5. To provide a method for locating transducers within thepassenger compartment at specific locations such that a high reliabilityof classification of objects and their position is obtained from thesignals generated by the transducers.

[0061] 6. To provide a method for combining a variety of transducersincluding seatbelt payout sensors, seatbelt buckle sensors, seatposition sensors, seatback position sensors, and weight sensors into asystem and adapt that system so as to provide a highly reliable occupantpresence and position system when used in combination withelectromagnetic, ultrasonic or other radiation sensors.

[0062] 7. To provide methods for controlling deployment of an occupantrestraint, optionally based on a determined position of the occupant.

[0063] 8. To provide methods for determining whether an object is achild seat, forward or rearward facing, and optionally controldeployment of an occupant restraint device based on such determination.

[0064] In order to achieve some of the objects set forth above,arrangements in accordance with the invention includes a plurality ofultrasonic transmitters for transmitting ultrasonic waves into thepassenger compartment, each operating at a distinct transmittingfrequency and positioned at a distinct location relative to the othertransmitter(s), at least one receiver disposed so as to receive from thepassenger compartment ultrasonic waves transmitted from a transmitterand modified by passing through at least part of the passengercompartment, and a processor operatively coupled to the receiver(s).Based on the ultrasonic waves received by the receiver(s), the processordetermines whether an object is located in the passenger compartment,determines the location of the object and/or controls deployment of asafety restraint device. It is not required that the processor make aseparate determination that an object is present before determining itslocation or to deploy the safety restraint device. Rather, the processorcan make a determination directly as to whether to deploy the restraintdevice based on the ultrasonic waves without making an initialdetermination that an occupant is present. The determination by theprocessor may entail the use of pattern recognition techniques, such asa neural network or sensor fusion.

[0065] In more specific embodiments, each receiver and transmitter maybe arranged to form a transducer. One transmitter (or transducer) may bearranged on a ceiling of the vehicle and a second transmitter (ortransducer) arranged at a different location in the vehicle, e.g., onthe dashboard, such that a first axis connecting the first and secondtransmitters is substantially parallel to a second axis traversing avolume in a passenger compartment of the vehicle above a seat in whichan object is situated. A third transmitter (or transducer) may beprovided on or adjacent an interior side surface of the passengercompartment. A fourth transmitter (or transducer) may be provided on oradjacent an interior side surface of the passenger compartment.

[0066] Other arrangements include a first receiver arranged on a ceilingof the vehicle, a second receiver arranged at a different location inthe vehicle than the first receiver such that a first axis connectingthe first and second receivers is substantially parallel to a secondaxis traversing a volume in a passenger compartment of the vehicle abovea seat in which an object is situated, and a third receiver arranged ata different location in the passenger compartment than the first andsecond receivers. Each receiver comprising distance measurement meanssuch that a first distance from the first receiver to the object isobtained based on the output of the first receiver; a second distancefrom the second receiver to the object is obtained based on the outputof the second receiver and a third distance from the third receiver tothe object is obtained based on the output of the third receiver. Aprocessor analyzes these distances and determines if an object ispresent based thereon, determines the location of the object and/orcontrols deployment of a safety restraint device. The processor does nothave to determine whether an object is present prior to formulating thecontrol signal for the restraint device but rather can make a directdetermination as to the manner in which the restraint device will becontrolled based on the distances as obtained from the receivers.

[0067] In other, more specific embodiments, the receivers are arrangedto receive ultrasonic or electromagnetic radiation and may all be of thesame type. A fourth receiver may be arranged at a different location inthe passenger compartment than the first, second and third receivers andcomprise distance measurement means. The processor analyzes the distancebetween the fourth receiver and the object when determining if an objectis present, determining the location of the object or controlling of therestraint device. In the event that the receivers receive, e.g.,ultrasonic waves, then one or more transmitters is/are provided fortransmitting waves into the passenger compartment.

[0068] Another embodiment of a method for controlling deployment of anoccupant restraint device in a vehicle comprises the steps of arranginga first ultrasonic transducer on a ceiling of the vehicle and a secondultrasonic transducer at a different location in the vehicle (e.g., onthe dashboard) such that a first axis connecting the first and secondtransducers is substantially parallel to a second axis traversing avolume in a passenger compartment of the vehicle above a seat in whichan object is situated, transmitting ultrasonic waves from the firsttransducer into the passenger compartment, receiving ultrasonic wavesreflected off the object in the passenger compartment at the firsttransducer and calculating a first distance from the first transducer tothe object based on the time difference between the transmitted waveswhen transmitted from the first transducer and the reflected waves whenreceived at the first transducer. In a similar manner, differentultrasonic waves are transmitted from the second transducer into thepassenger compartment, ultrasonic waves reflected off the object in thepassenger compartment are received at the second transducer and a seconddistance from the second transducer to the object is calculated based onthe time difference between the transmitted waves when transmitted fromthe second transducer and the reflected waves when received at thesecond transducer. Deployment of the occupant restraint device and/or adetermination of whether the object is a child seat is/are made based onthe first distance and the second distance.

[0069] Control of deployment of the occupant restraint device may entailsuppressing deployment of the occupant restraint device or if therestraint device includes an airbag, controlling a rate of generation ofa gas used to inflate the airbag and/or an amount of gas generated forinflating the airbag.

[0070] A third transducer may be arranged, e.g., on or adjacent aninterior side surface of the passenger compartment, to transmitdifferent ultrasonic waves into the passenger compartment and receiveultrasonic waves reflected off the object in the passenger compartment.The distance from the third transducer to the object is calculated basedon the time difference between the transmitted waves when transmittedfrom the third transducer and the reflected waves when received at thethird transducer. This distance is used in consideration of the controlof the deployment of the airbag and/or the determination as to whetherthe object is a child seat. A fourth transducer may also be arranged totransmit different ultrasonic waves into the passenger compartment andreceive ultrasonic waves reflected off the object in the passengercompartment. A fourth distance from the fourth transducer to the objectis calculated based on the time difference between the transmitted waveswhen transmitted from the fourth transducer and the reflected waves whenreceived at the fourth transducer. This distance is also used inconsideration of the control of the deployment of the airbag and/or thedetermination as to whether the object is a child seat.

[0071] Another method for controlling deployment of an occupantrestraint device in a vehicle and/or determining whether an object in aseat is a child seat comprises the steps of arranging a first receiveron a ceiling of the vehicle and a second receiver at a differentlocation in the vehicle such that a first axis connecting the first andsecond receivers is substantially parallel to a second axis traversing avolume in a passenger compartment of the vehicle above a seat in whichan object is situated, mounting a third receiver at a different locationin the passenger compartment than the first and second receivers, eachreceiver comprising distance measurement means, calculating a firstdistance from the first receiver to the object based on the output ofthe first receiver, calculating a second distance from the secondreceiver to the object based on the output of the second receiver, andcalculating a third distance from the third receiver to the object basedon the output of the third receiver. Deployment of the occupantrestraint device and/or the determination as to whether the object is achild seat is/are made based on the first distance, the second distanceand the third distance. The receivers may be constructed to receiveultrasonic radiation or electromagnetic radiation and may all be of thesame type.

[0072] Another method for controlling deployment of an occupantrestraint device in a vehicle and/or determining whether an object in aseat is a child seat comprises the steps of transmitting ultrasonicwaves from a first transducer into a passenger compartment of thevehicle, receiving waves reflected off an object in the passengercompartment at the first transducer, calculating a first distance fromthe first transducer to the object based on the time difference betweenthe transmitted waves when transmitted from the first transducer and thereflected waves when received by the first transducer, transmittingdifferent ultrasonic waves from a second transducer into the passengercompartment, receiving waves reflected off the object in the passengercompartment at the second transducer and calculating a second distancefrom the second transducer to the object based on the time differencebetween the transmitted waves when transmitted by the second transducerand the reflected waves when received by the second transducer.Deployment of the occupant restraint device and/or the determination asto whether the object is a child seat is/are made based on the firstdistance and the second distance. The distance calculation stepscomprise the step of applying an algorithm generated by means of apattern recognition algorithm generating program (e.g., a neural networkcomputer program) based on the time distribution of the echo pattern ofthe reflected waves in order to determine the distance from therespective transducer to the object.

[0073] In one embodiment of a method of developing a system fordetermining the occupancy state of a seat in a passenger compartment ofa vehicle comprises the steps of mounting transducers in the vehicle,which transducers would be affected by the occupancy state of the seat,forming at least one database comprising multiple data sets, each dataset representing a different occupancy state of the seat and beingformed by receiving data from the transducers while the seat is in thatoccupancy state, and processing the data received from the transducers,and creating a first algorithm from the database(s) capable of producingan output indicative of the occupancy state of the seat upon inputting anew data set representing an occupancy state of the seat. The new dataset would be formed, e.g., during use of the vehicle after the algorithmis installed in the control circuitry of the vehicle. The firstalgorithm may be created by inputting the database(s) into analgorithm-generating program, and running the algorithm-generatingprogram to produce the first algorithm. The first algorithm could be aneural network algorithm, in which case, the back propagation methodcould be used when generating the neural network algorithm.

[0074] The occupancy states of the seat include occupancy of the seat byan object selected from the group comprising occupied and unoccupiedrear facing infant seats, forward facing humans, out-of-position humans,occupied and unoccupied forward facing child seats and empty seats. Theoccupancy states of the seat should also include occupancy by theobjects in multiple orientations and/or having at least one accessoryselected from a non-exclusive group comprising newspapers, books, maps,bottles, toys, hats, coats, boxes, bags and blankets.

[0075] The data can be pre-processed prior to being formed into the datasets. This may entail using data created from features of the data inthe data set, which features might be selected from a group comprisingthe normalization factor, the number of data points prior to a peak, thetotal number of peaks, and the mean or variance of the data set. Also,the data sets could be mathematically transformed using normalization,truncation, logarithmic transformation, sigmoid transformation,thresholding, averaging the data over time, Fourier transforms and/orwavelet transforms. Further, pre-processing could entail subtractingdata in one data set from the corresponding data in another data set tocreate a third data set of differential data.

[0076] The processing step may comprise the step of converting theanalog data from the transducers to digital data and combining thedigital data from a plurality of the transducers to form a vectorcomprising a string of data from each of the transducers. As such, thefirst algorithm is created such that upon inputting a vector from a newdata set will produce an output representing the occupancy state of thevehicle seat. The vectors in the database can be normalized so that allvalues of the data that comprise each vector are between a maximum and aminimum.

[0077] Another disclosed method of developing a system for determiningthe occupancy state of the vehicle seat in the passenger compartment ofa vehicle comprises the steps of forming data sets by obtaining datarepresentative of various occupying objects at various positions in thepassenger compartment and operating on at least a portion of the data toreduce the magnitude of the largest data values in a data set relativeto the smallest data values, forming a database comprising multiple datasets, and creating an algorithm from the database capable of producingan output indicative of the occupancy state of the vehicle seat uponinputting a data set representing an occupancy state of the seat.Operating on the data may entail using an approximate logarithmictransformation function.

[0078] A disclosed method of developing a database for use in developinga system for determining the occupancy state of a vehicle seat inaccordance with the invention comprises the steps of mountingtransducers in the vehicle and which would be affected by the occupancystate of the seat, providing the seat with an initial occupancy state,receiving data from the transducers, processing the data from thetransducers to form a data set representative of the initial occupancystate of the vehicle seat, changing the occupancy state of the seat andrepeating the data collection process to form another data set,collecting at least 1000 data sets into a first database, eachrepresenting a different occupancy state of the seat and creating analgorithm from the first database which correctly identifies theoccupancy state of the seat for most of the data sets in the firstdatabase. The algorithm is tested using a second database of data setswhich were not used in the creation of the algorithm. The occupancystates in the second database are which were not correctly identified bythe algorithm are identified and new data comprising similar occupancystates to the incorrectly identified states is collected. The new datais combined with the first database, a new algorithm is created based onthe combined database and this process is repeated until the desiredaccuracy of the algorithm is achieved.

[0079] Another disclosed method of developing a system for determiningthe occupancy state of a passenger compartment seat of a vehiclecomprises the steps of mounting a plurality of ultrasonic transducers inthe vehicle (which transducers would be affected by the occupancy stateof the seat), receiving an analog signal from each of the transducers,processing the analog signals from the transducers to form a data setcomprising multiple data values from each transducer representative ofthe occupancy state of the vehicle, the data processing comprising thesteps of demodulation, sampling and digitizing of the transducer data tocreate a data set of digital data, forming a database comprisingmultiple data sets and creating at least one algorithm from the databasecapable of producing an output indicative of the occupancy state of theseat upon inputting a new data set representing an occupancy state ofthe seat.

[0080] Still another disclosed method of developing a system fordetermining the occupancy state of a vehicle seat in a passengercompartment of a vehicle comprises the steps of mounting a set oftransducers on the vehicle, receiving data from the transducers,processing the data from transducers to form a data set representativeof the occupancy state of the vehicle, forming a database comprisingmultiple data sets, creating an algorithm from the database capable ofproducing an output indicative of the occupancy state of the vehicleseat upon inputting a new data set, and developing a measure of systemaccuracy. At least one transducer is removed from the transducer set, anew database is created containing data only from the reduced number oftransducers, a new algorithm is developed based on the new database andtested to determine the new system accuracy. The process of removingtransducers, algorithm development and testing is continued until theminimum number of sensors is determined which produces an algorithmhaving desired accuracy. The transducers are selected from a groupconsisting of ultrasonic transducers, optical sensors, capacitivesensors, weight sensors, seat position sensors, seatback positionsensors, seat belt buckle sensors, seatbelt payout sensors, infraredsensors, inductive sensors and radar sensors.

[0081] Yet another disclosed method of developing a system fordetermining the occupancy state of the driver and passenger seats of avehicle comprises the steps of mounting ultrasonic transducers havingdifferent transmitting and receiving frequencies in a vehicle such thattransducers having adjacent frequencies are not within the directultrasonic field of each other, receiving data from the transducers,processing the data from the transducers to form a data setrepresentative of the occupancy state of the vehicle, forming at leastone database comprising multiple data sets and creating at least onealgorithm from the at least one database capable of producing an outputindicative of the occupancy state of a vehicle seat upon inputting a newdata set.

[0082] These and other objects and advantages will become apparent fromthe following description of the preferred embodiments of the vehicleidentification and monitoring system of this invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The following drawings are illustrative of embodiments of thesystem developed or adapted using the teachings of this invention andare not meant to limit the scope of the invention as encompassed by theclaims. In particular, the illustrations below are limited to themonitoring of the front passenger seat for the purpose of describing thesystem. Naturally, the invention applies as well to adapting the systemto the other seating positions in the vehicle and particularly to thedriver position.

[0084]FIG. 1 shows a seated-state detecting unit developed in accordancewith the present invention and the connections between ultrasonic orelectromagnetic sensors, a weight sensor, a reclining angle detectingsensor, a seat track position detecting sensor, a heartbeat sensor, amotion sensor, a neural network circuit, and an airbag system installedwithin a vehicle compartment;

[0085]FIG. 2 is a perspective view of a vehicle containing two adultoccupants on the front seat with the vehicle shown in phantomillustrating one preferred location of the ultrasonic transducers placedaccording to the methods taught in this invention.

[0086]FIG. 3 is a view as in FIG. 2 with the passenger occupant replacedby a child in a forward facing child seat.

[0087]FIG. 4 is a view as in FIG. 2 with the passenger occupant replacedby a child in a rearward facing child seat.

[0088]FIG. 5 is a view as in FIG. 2 with the passenger occupant replacedby an infant in an infant seat.

[0089]FIG. 6 is a diagram illustrating the interaction of two ultrasonicsensors and how this interaction is used to locate a circle is space.

[0090]FIG. 7 is a view as in FIG. 2 with the occupants removedillustrating the location of two circles in space and how they intersectthe volumes characteristic of a rear facing child seat and a largeroccupant.

[0091]FIG. 8 illustrates a preferred mounting location of athree-transducer system.

[0092]FIG. 9 illustrates a preferred mounting location of afour-transducer system.

[0093]FIG. 10 is a plot showing the target volume discrimination for twotransducers.

[0094]FIG. 11 illustrates a preferred mounting location of aeight-transducer system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0095] System Adaptation involves the process by which the hardwareconfiguration and the software algorithms are determined for aparticular vehicle. Each vehicle model or platform will most likely havea different hardware configuration and different algorithms. Some of thevarious aspects that make up this process are as follows:

[0096] The determination of the mounting location and aiming of thetransducers.

[0097] The determination of the transducer field angles

[0098] The use of a neural network algorithm generating program such ascommercially available from NeuralWare to help generate the algorithms.

[0099] The process of the collection of data in the vehicle for neuralnetwork training purposes.

[0100] The method of automatic movement of the vehicle seats etc. whiledata is collected

[0101] The determination of the quantity of data to acquire and thesetups needed to achieve a high system accuracy, typically severalhundred thousand vectors.

[0102] The collection of data in the presence of varying environmentalconditions such as with thermal gradients.

[0103] The photographing of each data setup.

[0104] The makeup of the different databases and the use of threedifferent databases.

[0105] The method by which the data is biased to give higherprobabilities for forward facing humans.

[0106] The automatic recording of the vehicle setup including seat, seatback, headrest, window, visor, armrest positions to help insure dataintegrity.

[0107] The use of a daily setup to validate that the transducerconfiguration has not changed.

[0108] The method by which bad data is culled from the database.

[0109] The inclusion of the Fourier transforms and other pre-processorsof the data in the training process. a The use of multiple networklevels, for example, for categorization and position.

[0110] The use of multiple networks in parallel.

[0111] The use of post processing filters and the particularities ofthese filters.

[0112] The addition of fuzzy logic or other human intelligence basedrules.

[0113] The method by which vector errors are corrected using, forexample, a neural network.

[0114] The use of neural works as the pattern recognition algorithmgenerating system.

[0115] The use of back propagation neural networks from training.

[0116] The use of vector normalization.

[0117] The use of feature extraction techniques including:

[0118] The number of data points prior to a peak.

[0119] The normalization factor.

[0120] The total number of peaks.

[0121] The vector mean or variance.

[0122] The use of other computational intelligence systems such as thegenetic algorithms

[0123] The use the data screening techniques.

[0124] The techniques used to develop a stable network including theconcepts of a old and a new network.

[0125] The time spent or the number of iterations spent in, and methodof, arriving at a stable network.

[0126] The technique where a small amount of data is collected firstsuch as 16 sheets followed by a complete data collection sequence.

[0127] The process of adapting the system to the vehicle begins with asurvey of the vehicle model. Any existing sensors, such as seat positionsensors, seat back sensors, etc., are immediate candidates for inclusioninto the system. Input from the customer will determine what types ofsensors would be acceptable for the final system. These sensors caninclude: seat structure mounted weight sensors, pad type weight sensors,pressure type weight sensors, seat fore and aft position sensors, seatvertical position sensors, seat angular position sensors, seat backposition sensors, headrest position sensors, ultrasonic occupantsensors, optical occupant sensors, capacitive sensors, inductivesensors, radar sensors, vehicle velocity and acceleration sensors, brakepressure, seatbelt force, payout and buckle sensors, etc. A candidatearray of sensors is then chosen and mounted onto the vehicle.

[0128] The vehicle is also instrumented so that data input by humans isminimized. Thus, the positions of the various components in the vehiclesuch as the seats, windows, sun visor, armrest, etc. are automaticallyrecorded. Also, the position of the occupant while data is being takenis also absolutely recorded through a variety of techniques such asdirect ultrasonic ranging sensors, optical ranging sensors, radarranging sensors, optical tracking sensors etc. Cameras are alsoinstalled to take a picture of the setup to correspond to each vector ofdata collected or at some other appropriate frequency.

[0129] A standard set of vehicle setups is chosen for initial trial datacollection purposes. Typically, the initial trial will consist ofbetween 20,000 and 100,000 setups.

[0130] Initial digital data collection now proceeds for the trial setupmatrix. The data is collected from the transducers, digitized andcombined to form to a vector of input data for analysis by a neuralnetwork program. This analysis should yield a training accuracy ofnearly 100%. If this is not achieved, then additional sensors are addedto the system or the configuration changed and the data collection andanalysis repeated.

[0131] In addition to a variety of seating states for objects in thepassenger compartment, the trial database will also includeenvironmental effects such as thermal gradients caused by heat lamps andthe operation of the air conditioner and heater. A sample of such amatrix is presented in Appendix 1. After the neural network has beentrained on the trial database, the trial database will be scanned forvectors that yield erroneous results (which would likely be consideredbad data). A study of those vectors along with vectors from associatedin time cases are compared with the photographs to determine whetherthere is erroneous data present. If so, an attempt is made to determinethe cause of the erroneous data. If the cause can be found, for exampleif a voltage spike on the power line corrupted the data, then the vectorwill be removed from the database and an attempt is made to correct thedata collection process so as to remove such disturbances.

[0132] At this time, some of the sensors may be eliminated from thesensor matrix. This can be determined during the neural network analysisby selectively eliminating sensor data from the analysis to see what theeffect if any results. Caution should be exercised here, however, sinceonce the sensors have been initially installed in the vehicle, itrequires little additional expense to use all of the installed sensorsin future data collection and analysis.

[0133] The neural network that has been developed in this first phase isused during the data collection in the next phases as a instantaneouscheck on the integrity of the new vectors being collected. Occasionally,a voltage spike or other environmental disturbance will momentarilyeffect the data from some transducers. It is important to capture thisevent to first eliminate that data from the database and second toisolate the cause of the erroneous data.

[0134] The next set of data to be collected is the training database.This will be the largest database initially collected and will coversuch setups as listed, for example, in Appendix 1. The trainingdatabase, which may contain 500,000 or more vectors, will be used tobegin training of the neural network. While this is taking placeadditional data will be collected according to Appendix 1 of theindependent and validation databases. The training database has beenselected so that it uniformly covers all seated states that are known tobe likely to occur in the vehicle. The independent database may besimilar in makeup to the training database or it may evolve to moreclosely conform to the occupancy state distribution of the validationdatabase. During the neural network training, the independent databaseis used to check the accuracy of the neural network and to reject acandidate neural network design if its accuracy, measured against theindependent database, is less than that of a previous networkarchitecture.

[0135] Although the independent database is not actually used in thetraining of the neural network, nevertheless, it has been found that itsignificantly influences the network structure. Therefore, a thirddatabase, the validation or real world database, is used as a finalaccuracy check of chosen system. It is the accuracy against thisvalidation database that is considered to be the system accuracy. Thevalidation database is composed of vectors taken from setups whichclosely correlate with vehicle occupancy in real cars on the roadway.Initially the training database is the largest of the three databases.As time and resources permit the independent database, which perhapsstarts out with 100,000 vectors, will continue to grow until it becomesapproximately the same size as the training database. The validationdatabase, on the other hand, will typically start out with as few as50,000 vectors. However, as the hardware configuration is frozen, thevalidation database will continuously grow until, in some cases, itactually becomes larger than the training database. This is because nearthe end of the program, vehicles will be operating on highways and datawill be collected in real world situations. If in the real world tests,system failures are discovered this can lead to additional data beingtaken for both the training and independent databases as well as thevalidation database.

[0136] Once a network has been trained using all of the available datafrom all of the transducers, it is expected that the accuracy of thenetwork will be very close to 100%. It is usually not practical to useall of the transducers that have been used in the training of the systemfor final installation in real production vehicle models. This isprimarily due to cost and complexity considerations. Usually theautomobile manufacturer will have an idea of how many sensors would beacceptable for installation in a production vehicle. For example, thedata may have been collected using 20 different transducers but theautomobile manufacturer may restrict the final selection to 6transducers. The next process, therefore, is to gradually eliminatesensors to determine what is the best combination of six sensors, forexample, to achieve the highest system accuracy. Ideally, a series ofnetworks would be trained using all combinations of six sensors from the20 available. The activity would require a prohibitively long time.Certain constraints can be factored into the system from the beginningto start the pruning process. For example, it would probably not makesense to have both optical and ultrasonic sensors present in the samesystem since it would complicate the electronics. In fact, theautomobile manufacturer may have decided initially that an opticalsystem would be too expensive and therefore would not be considered. Theinclusion of optical sensors, therefore, serves as a way of determiningthe loss in accuracy as a function of cost. Various constraints,therefore, usually allow the immediate elimination of a significantnumber of the initial group of sensors. This elimination and thetraining on the remaining sensors provides the resulting accuracy lossthat results.

[0137] The next step is to remove each of the sensors one at a time anddetermine which sensor has the least effect on the system accuracy. Thisprocess is then repeated until the total number of sensors has beenpruned down to the number desired by the customer. At this point, theprocess is reversed to add in one at a time those sensors that wereremoved at previous stages. It has been found, for example, that asensor that appears to be unimportant during the early pruning processcan become very important later on. Such a sensor may add a small amountof information due to the presence of various other sensors. Whereas thevarious other sensors, however, may yield less information than stillother sensors and, therefore may have been removed during the pruningprocess. Reintroducing the sensor that was eliminated early in the cycletherefore can have a significant effect and can change the final choiceof sensors to make up the system.

[0138] The above method of reducing the number of sensors that make upthe system is but one of a variety approaches which have applicabilityin different situations. In some cases a Monte Carlo or otherstatistical approach is warranted, whereas in other cases a design ofexperiments approach has proven to be the most successful. In manycases, an operator conducting this activity becomes skilled and after awhile knows intuitively what set of sensors is most likely to yield thebest results. During the process it is not uncommon to run multiplecases on different computers simultaneously. Also, during this process,a database of the cost of accuracy is generated. The automobilemanufacturer, for example, may desire to have the total of 6 transducersin the final system, however, when shown the fact that the addition ofone or two additional sensors substantially increases the accuracy ofthe system, the manufacturer may change his mind. Similarly, the initialnumber of sensors selected may be 6 but the analysis could show that 4sensors give substantially the same accuracy as 6 and therefore theother 2 can be eliminated at a cost saving.

[0139] While the pruning process is occurring, the vehicle is subjectedto a variety of road tests and would be subjected to presentations tothe customer. The road tests are tests that are run at differentlocations than where the fundamental training took place. It has beenfound that unexpected environmental factors can influence theperformance of the system and therefore these tests can provide criticalinformation. The system, therefore, which is installed in the testvehicle should have the capability of recording system failures. Thisrecording includes the output of all of the sensors on the vehicle aswell as a photograph of the vehicle setup that caused the error. Thisdata is later analyzed to determine whether the training, independent orvalidation setups need to be modified and/or whether the sensors orpositions of the sensors require modification.

[0140] Once the final set of sensors has been chosen, the vehicle isagain subjected to real world testing on highways and at customerdemonstrations. Once again any failures are recorded. In this case,however, since the total number of sensors in the system is probablysubstantially less than the initial set of sensors, certain failures areto be expected. All such failures, if expected, are reviewed carefullywith the customer to be sure that the customer recognizes the systemfailure modes and is prepared to accept the system with those failuremodes.

[0141] The system described so far has been based on the use of a singleneural network. It is frequently necessary to use multiple neuralnetworks or other pattern recognition systems. For example, fordetermining the occupancy state of a vehicle seat there are really tworequirements. The first requirement is to establish what is occupyingthe seat and the second requirement is to establish where that object islocated. Generally, a great deal of time, typically many seconds, isavailable for determining whether a forward facing human or an occupiedor unoccupied rear facing child seat, for example, occupies the vehicleseat. On the other hand, if the driver of the car is trying to avoid anaccident and is engaged in panic braking, the position of an unbeltedoccupant can be changing rapidly as he or she is moving toward theairbag. Thus, the problem of determining the location of an occupant istime critical. Typically, the position of the occupant in suchsituations must be determined in less than 20 milliseconds. There is noreason for the system to have to determine that a forward facing humanbeing is in the seat while simultaneously determining where that forwardfacing human being is. The system already knows that the forward facinghuman being is present and therefore all of the resources can be used todetermine the occupant's position. Thus, in this situation a dual levelneural network can be advantageously used. The first level determinesthe occupancy of the vehicle seat and the second level determines theposition of that occupant. In some rare situations, it has beendemonstrated that multiple neural networks used in parallel can providesome benefit. This will be discussed in more detail below.

[0142] The data that is fed to the pattern recognition system typicallywill usually not be the raw vectors of data as captured and digitizedfrom the various transducers. Typically, a substantial amount ofpreprocessing of the data is undertaken to extract the importantinformation from the data that is fed to the neural network. This isespecially true in optical systems and where the quantity of dataobtained, if all were used by the neural network, would require veryexpensive processors. The techniques of preprocessing data will not bedescribed in detail here. However, the preprocessing techniquesinfluence the neural network structure in many ways. For example, thepreprocessing used to determine what is occupying a vehicle seat istypically quite different from the preprocessing used to determine thelocation of that occupant. Some particular preprocessing concepts willbe discussed in more detail below.

[0143] Once the pattern recognition system has been applied to thepreprocessed data, one or more decisions are available as output. Theoutput from the pattern recognition system is usually based on asnapshot of the output of the various transducers. Thus, it representsone epoch or time period. The accuracy of such a decision can usually besubstantially improved if previous decisions from the patternrecognition system are also considered. In the simplest form, which istypically used for the occupancy identification stage, the results ofmany decisions are averaged together and the resulting averaged decisionis chosen as the correct decision. Once again, however, the situation isquite different for dynamic out-of-position. The position of theoccupant must be known at that particular epoch and cannot be averagedwith his previous position. On the other hand, there is information inthe previous positions that can be used to improve the accuracy of thecurrent decision. For example, if the new decision says that theoccupant has moved six inches since the previous decision, and, fromphysics, it is known that this could not possibly take place, than abetter estimate of the current occupant position can be made byextrapolating from earlier positions. Alternately, an occupancy positionversus time curve can be fitted using a variety of techniques such asthe least squares regression method, to the data from previous 10epochs, for example. This same type of analysis could also be applied tothe vector itself rather than to the final decision thereby correctingthe data prior to its being entered into the pattern recognition system.

[0144] A pattern recognition system, such as a neural network, cansometimes make totally irrational decisions. This typically happens whenthe pattern recognition system is presented with a data set or vectorthat is unlike any vector that has been in its training set. The varietyof seating states of a vehicle is unlimited. Every attempt is made toselect from that unlimited universe a set of representative cases.Nevertheless, there will always be cases that are significantlydifferent from any that have been previously presented to the neuralnetwork. The final step, therefore, to adapting a system to a vehicle,is to add a measure of human intelligence. Sometimes this goes under theheading of fuzzy logic and the resulting system has been termed in somecases a neural fuzzy system. In some cases, this takes the form of anobserver studying failures of the system and coming up with rules andthat say, for example, that if sensor A perhaps in combination withanother sensor produces values in this range than the system should beprogrammed to override the pattern recognition decision and substitutetherefor a human decision.

[0145] An example of this appears in R. Scorcioni, K. Ng, M. M. Trivedi,N. Lassiter; “MoNiF: A Modular Neuro-Fuzzy Controller for Race CarNavigation”; In Proceedings of the 1997 IEEE Symposium on ComputationalIntelligence and Robotics Applications, Monterey, Calif., USA July 1997and describes the case of where an automobile was designed forautonomous operation and trained with a neural network, in one case, anda neural fuzzy system in another case. As long as both vehicles operatedon familiar roads both vehicles performed satisfactorily. However, whenplaced on an unfamiliar road, the neural network vehicle failed whilethe neural fuzzy vehicle continue to operate successfully. Naturally, ifthe neural network vehicle had been trained on the unfamiliar road, itmight very well have operated successful. Nevertheless, the criticalfailure mode of neural networks that most concerns people is thisuncertainty as to what a neural network will do when confronted with anunknown state.

[0146] One aspect, therefore, of adding human intelligence to thesystem, is to ferret out those situations where the system is likely tofail. Unfortunately, in the current state-of-the-art, this is largely atrial and error activity. One example is that if the range of certainparts of vector falls outside of the range experienced during training,the system defaults to a particular state. In the case of suppressingdeployment of one or more airbags, or other occupant protectionapparatus, this case would be to enable airbag deployment even if thepattern recognition system calls for its being disabled.

[0147] The foregoing description is applicable to the systems describedin the following drawings and the connection between the foregoingdescription and the systems described below will be explained below.However, it should be appreciated that the systems shown in the drawingsdo not limit the applicability of the methods described above.

[0148] Referring to the accompanying drawings wherein like referencenumbers designate the same or similar elements, FIG. 1 shows a passengerseat 1 to which an adjustment apparatus including a seated-statedetecting system developed according to the present invention may beapplied. The seat 1 includes a horizontally situated bottom seat portion2 and a vertically oriented back portion 3. The seat portion 2 isprovided with weight measuring means, i.e., one or more weight sensors 6and 7, that determine the weight of the object occupying the seat, ifany, as well as enable the weight distribution of the object to beobtained. The coupled portion between the seated portion 2 and the backportion 3 (also referred to as the seatback) is provided with areclining angle detecting sensor 9, which detects the tilted angle ofthe back portion 3 relative to the seat portion 2. The seat portion 2 isprovided with a seat track position-detecting sensor 10. The seat trackposition detecting sensor 10 fulfills a role of detecting the quantityof movement of the seat 1 which is moved from a back reference position,indicated by the dotted chain line. Embedded within the seatback 3 is aheartbeat sensor 31 and a motion sensor 33. Attached to the headliner ofthe vehicle is a capacitance sensor 32. The seat 1 may be the driverseat, the front passenger seat or any other seat in a motor vehicle aswell as other seats in transportation vehicles or seats innon-transportation applications.

[0149] The weight measuring means, such as the sensors 6 and 7, areassociated with the seat, and can be mounted into or below the seatportion 2 or on the seat structure, for example, for measuring theweight applied onto the seat. The weight may be zero if no occupyingitem is present. Sensors 6 and 7 may represent a plurality of differentsensors which measure the weight applied onto the seat at differentportions thereof or for redundancy purposes, for example, such as bymeans of an airbag or bladder 5 in the seat portion 2. The bladder 5 mayhave one or more compartments. Such sensors may be in the form ofstrain, force or pressure sensors which measure the force or pressure onthe seat portion 2 or seat back 3, displacement measuring sensors whichmeasure the displacement of the seat surface or the entire seat 1 suchas through the use of strain gages mounted on the seat structuralmembers, such as 7, or other appropriate locations, or systems whichconvert displacement into a pressure wherein a pressure sensor can beused as a measure of weight.

[0150] An ultrasonic or optical sensor system 12 is mounted on the upperportion of the front pillar, A-Pillar, of the vehicle and a similarsensor system 11 is mounted on the upper portion of the intermediatepillar, B-Pillar. The outputs of the transducers 11 and 12 are input toa band pass filter 20 through a multiplex circuit 19 which is switchedin synchronization with a timing signal from the ultrasonic sensor drivecircuit 18, and then is amplified by an amplifier 21. The band passfilter 20 removes a low frequency wave component from the output signaland also removes some of the noise. The envelope wave signal is input toan analog/digital converter (ADC) 22 and digitized as measured data. Themeasured data is input to a processing circuit 23, which is controlledby the timing signal which is in turn output from the sensor drivecircuit 18.

[0151] Each of the measured data is input to a normalization circuit 24and normalized. The normalized measured data is input to the neuralnetwork circuit 25 as wave data.

[0152] The output of the weight sensor(s) 6 and 7 is amplified by anamplifier 26 coupled to the weight sensor(s) 6 and 7 and the amplifiedoutput is input to the analog/digital converter 27.

[0153] The reclining angle detecting sensor 9 and the seat trackposition-detecting sensor 10 are connected to appropriate electroniccircuits. For example, a constant-current can be supplied from aconstant-current circuit to the reclining angle detecting sensor 9, andthe reclining angle detecting sensor 9 converts a change in theresistance value on the tilt of the back portion 3 to a specificvoltage. This output voltage is input to an analog/digital converter 28as angle data, i.e., representative of the angle between the backportion 3 and the seat portion 2. Similarly, a constant current can besupplied from a constant-current circuit to the seat track positiondetecting sensor 10 and the seat track position detecting sensor 10converts a change in the resistance value based on the track position ofthe seat portion 2 to a specific voltage. This output voltage is inputto an analog/digital converter 29 as seat track data. Thus, the outputsof the reclining angle-detecting sensor 9 and the seat trackposition-detecting sensor 10 are input to the analog/digital converters(ADC) 28 and 29, respectively. Each digital data value from the ADCs28,29 is input to the neural network circuit 25. A more detaileddescription of this and similar systems can be found in co-pendingpatent application, which is included herein by reference. The systemdescribed above is one example of many systems that can be designedusing the teachings of this invention for detecting the occupancy stateof the seat of a vehicle.

[0154] A section of the passenger compartment of an automobile is showngenerally as 100 in FIG. 2. A driver 101 of a vehicle sits on a seat 102behind a steering wheel, not shown, and an adult passenger 103 sits onseat 104 on the passenger side. Two transmitter and receiver assemblies110 and 111, also referred to herein as transducers, are positioned inthe passenger compartment 100, one transducer 110 is arranged on theheadliner adjacent or in proximity to the dome light and the othertransducer 111 is arranged on the center of the top of the dashboard.The methodology leading to the placement of these transducers is centralto the instant invention as explained in detail below. In thissituation, the system developed in accordance with this invention willreliably detect that an occupant is sitting on seat 104 and deploymentof the airbag is enabled in the event that the vehicle experiences acrash. Transducers 110, 111 are placed with their separation axisparallel to the separation axis of the head, shoulder and rear facingchild seat volumes of occupants of an automotive passenger seat and inview of this specific positioning, are capable of distinguishing thedifferent configurations. In addition to the ultrasonic transducers 110,111, weight-measuring sensors 210, 211, 212, 214 and 215 are alsopresent. These weight sensors may be of a variety of technologiesincluding, as illustrated here, strain-measuring transducers attached tothe vehicle seat support structure as described in more detail inco-pending patent application. Naturally other weight systems can beutilized including systems that measure the deflection of, or pressureon, the seat cushion. The weight sensors described here are meant to beillustrative of the general class of weight sensors and not anexhaustive list of methods of measuring occupant weight.

[0155] In FIG. 3, a forward facing child seat 120 containing a child 121replaces the adult passenger 103 as shown in FIG. 2. In this case, it isusually required that the airbag not be disabled in the event of anaccident. However, in the event that the same child seat is placed inthe rearward facing position as shown in FIG. 4, then the airbag isusually required to be disabled since deployment of the airbag in acrash can seriously injure or even kill the child. Furthermore, asillustrated in FIG. 5, if an infant 131 in an infant carrier 130 ispositioned in the rear facing position of the passenger seat, the airbagshould be disabled for the reasons discussed above. Instead of disablingdeployment of the airbag, the deployment could be controlled to provideprotection for the child, e.g., to reduce the force of the deployment ofthe airbag. It should be noted that the disabling or enabling of thepassenger airbag relative to the item on the passenger seat may betailored to the specific application. For example, in some embodiments,with certain forward facing child seats, it may in fact be desirable todisable the airbag and in other cases to deploy a depowered airbag. Theselection of when to disable, depower or enable the airbag, as afunction of the item in the passenger seat and its location, is madeduring the programming or training stage of the sensor system and, inmost cases, the criteria set forth above will be applicable, i.e.,enabling airbag deployment for a forward facing child seat and an adultin a proper seating position and disabling airbag deployment for arearward facing child seat and infant and for any occupant who isout-of-position and in close proximity to the airbag module. The sensorsystem developed in accordance with the invention may however beprogrammed according to other criteria.

[0156] Several systems using other technologies have been devised todiscriminate between the four cases illustrated above but none haveshown a satisfactory accuracy or reliability of discrimination. Some ofthese systems appear to work as long as the child seat is properlyplaced on the seat and belted in. So called “tag systems”, for example,whereby a device is placed on the child seat which iselectromagnetically sensed by sensors placed within the seat have notproven reliable by themselves but can add information to the overallsystem. When used alone, they function well as long as the child seat isrestrained by a seatbelt, but when this is not the case they have a highfailure rate. Since the seatbelt usage of the population of the UnitedStates is only about 50% at the present time, it is quite likely that asignificant percentage of child seats will not be properly belted ontothe seat and thus children will be subjected to injury and death in theevent of an accident.

[0157] The methodology of this invention was devised to solve thisproblem. To understand this methodology, consider two wave-based (e.g.,ultrasonic or electromagnetic) transmitters and receivers 110 and 111(transducers) which are connected by an axis AB in FIG. 6. Eachtransmitter radiates a signal which is primarily confined to a coneangle, called the field angle, with its origin at the transmitter. Forsimplicity, assume that the transmitter and receiver are the same devicealthough in some cases a separate device will be used for each function.When a transducer sends out a burst of waves, to thereby irradiate thepassenger compartment with ultrasonic radiation, and then receives areflection or modified radiation from some object in the passengercompartment (if the seat is empty, then the wave will reflect from theseat), the distance of the object from the transducer can be determinedby the time delay between the transmission of the waves and thereception of the reflected or modified waves.

[0158] When looking at a single transducer, it is not possible todetermine the direction to the object which is reflecting or modifyingthe signal but it is possible to know only how far that object is fromthe transducer, that is a single transducer enables a distancemeasurement but not a directional measurement. In other words, theobject may be at a point on the surface of a three-dimensional sphericalsegment having its origin at the transducer and a radius equal to thedistance. Consider two transducers, such as 110 and 111 in FIG. 6, andboth transducers receive a reflection from the same object, which isfacilitated by proper placement of the transducers, the timing of thereflections depends on the distance from the object to each respectivetransducer. If it is assumed for the purposes of this analysis that thetwo transducers act independently, that is, they only listen to thereflections of waves which they themselves transmitted, then eachtransducer knows the distance to the reflecting object but not itsdirection. If we assume that the transducer radiates ultrasound in alldirections within the field cone angle, each transducer knows that theobject is located on a spherical surface A′, B′ a respective knowndistance from the transducer, that is, each transducer knows that theobject is a specific distance from that transducer which may or may notbe the same distance between the other transducer and the same object.Since now there are two transducers, and the distance of the reflectingobject is known relative to each of the transducers, the actual locationof the object resides on a circle which is the intersection of the twospherical surfaces A′, and B′. This circle is labeled C in FIG. 6. Ateach point along circle C, the distance to the transducer 110 is thesame and the distance to the transducer 111 is the same. This, ofcourse, is strictly true only for ideal one-dimensional objects.

[0159] For many cases, the mere knowledge that the object lies on aparticular circle is sufficient since it is possible to locate thecircle such that the only time that an object lies on a particularcircle that its location is known. That is, the circle which passesthrough the area of interest otherwise passes through a volume where noobjects can occur. Thus, the mere calculation of the circle in thisspecific location, which indicates the presence of the object along thatcircle, provides valuable information concerning the object in thepassenger compartment which may be used to control or affect anothersystem in the vehicle such as the airbag system. This of course is basedon the assumption that the reflections to the two transducers are infact from the same object. Care must be taken in locating thetransducers such that other objects do not cause reflections that couldconfuse the system.

[0160]FIG. 7 for example illustrates two circles D and E, of interestwhich represent the volume which is usually occupied when the seat isoccupied by a person not in a child seat, C, or by a forward facingchild seat and the volume normally occupied by a rear facing child seat,respectively. Thus, if the circle generated by the system, (i.e., byappropriate processor means which receives the distance determinationfrom each transducer and creates the circle from the intersection of thespherical surfaces which represent the distance from the transducers tothe object) is at a location which is only occupied by an adultpassenger, the airbag would not be disabled since its deployment in acrash is desired. On the other hand, if a circle is at a locationoccupied only by a rear facing child seat, the airbag would be disabled.

[0161] The above discussion of course is simplistic in that it is nottake into account the volume occupied by the object or the fact thereflections from more than one object surface will be involved. Inreality, transducer B is likely to pickup the rear of the occupant'shead and transducer A, the front. This makes the situation moredifficult for an engineer looking at the data to analyze. It has beenfound that pattern recognition technologies are able to extract theinformation from these situations and through a proper application ofthese technologies, an algorithm can be developed, which when installedas part of the system for a particular vehicle, the system accuratelyand reliably differentiates between a forward facing and rear facingchild seat, for example, or an in-position or out-of-position forwardfacing human being. The system would also be designed to determine thepresence of an object based on the distances between the receivers andthe reflecting object and the location of the object relative to a fixedpoint of reference in the vehicle based on the distances between thereceivers and the reflecting object.

[0162] From the above discussion, a method of transducer location isdisclosed which provides unique information to differentiate between (i)a forward facing child seat or a forward properly positioned occupantwhere airbag deployment is desired and (ii) a rearward facing child seatand an out-of-position occupant where airbag deployment is not desired.Also, the location of the transducers enables the detection of thepresence of an object as well as the location of the object. Inactuality, the algorithm used to implement this theory does not directlycalculate the surface of spheres or the circles of interaction ofspheres. Instead, a pattern recognition system is used to differentiateairbag-deployment desired cases from those where the airbag should notbe deployed. For the pattern recognition system to accurately performits function, however, the patterns presented to the system must havethe requisite information. That is, a pattern of reflected waves from anoccupying item in a passenger compartment to various transducers must beuniquely different for cases where airbag deployment is desired fromcases where deployment is not desired. The theory described above and inmore detail below teaches how to locate transducers within the vehiclepassenger compartment so that the patterns of reflected waves will beeasily distinguishable for cases where airbag deployment is desired fromthose where deployment is not desired. In the case presented thus far,it has been shown that in some implementations the use of only twotransducers can result in the desired pattern differentiation when thevehicle geometry is such that two transducers can be placed such thatthe circles D (airbag enabled) and E (airbag disabled) fall outside ofthe transducer field cones except where they are in the critical regionswhere positive identification of the condition occurs. Thus, the aimingand field angle of the transducers are important factors to determine inadapting a system to a particular vehicle.

[0163] The use of only two transducers in a system is typically notacceptable since one or both of the transducers can be renderedinoperable by being blocked, for example, by a newspaper. Thus, it isdesirable to add a third transducer 112 as shown in FIG. 8 which nowprovides a third set of spherical surfaces relative to the thirdtransducer. Transducer 112 is positioned on the passenger side of theA-pillar (which is a preferred placement if the system is designed tooperate on the passenger side of the vehicle). Three spherical surfacesnow intersect in only two points and in fact, usually at one point ifthe aiming angles and field angles are properly chosen. Once again, thisdiscussion is only strictly true for a point object. For a real object,the reflections will come from different surfaces of the object, whichusually are at similar distances from the object. Thus, the addition ofa third transducer substantially improves system reliability. Finally,with the addition of a fourth transducer 113 as shown in FIG. 9, evengreater accuracy and reliability is attained. Transducer 113 ispositioned on the ceiling of the vehicle close to the passenger sidedoor. In FIG. 9, lines connecting the transducers C and D and thetransducers A and B are substantially parallel permitting an accuratedetermination of asymmetry and thereby object rotation. Thus, forexample, if the infant seat is placed on an angle as shown in FIG. 5,this condition can be determined and taken into account when thedecision is made to disable the deployment of the airbag.

[0164] The discussion above has centered on locating transducers anddesigning a system for determining whether the two target volumes, thatadjacent the airbag and that adjacent the upper portion of the vehicleseat, are occupied. Other systems have been described in the abovereferenced patents using a sensor mounted on or adjacent the airbagmodule and a sensor mounted high in the vehicle to monitor the spacenear the vehicle seat. Such systems use the sensors as independentdevices and do not use the combination of the two sensors to determinewhere the object is located. In fact, the location of such sensors isusually poorly chosen so that it is easy to blind either or both with anewspaper, for example. Furthermore, no system is known to have beendisclosed, except in patents and patent applications assigned to theassignee of this invention, which uses more than two transducers in sucha manner that one or more can be blocked without causing seriousdeterioration of the system. Again, the examples here have been for thepurpose of suppressing the deployment of the airbag when it is necessaryto prevent injury. The sensor system disclosed can be used for manyother purposes such as disclosed in the above-mentioned patentapplications assigned to the same assignee as the instant invention. Theability to use the sensors for these other applications in generallylacking in the systems disclosed in the other referenced patents.

[0165] Considering once again the condition of FIGS. 2-7 where twotransducers are used, a plot can be made showing the reflection times ofthe objects which are located in the region of curve E and curve F ofFIG. 7. This plot is shown on FIG. 10 where the c's representreflections from rear facing child seats from various tests where theseats were placed in a variety of different positions and similarly thes's and h's represent shoulders and heads respectively of variousforward facing human occupants. In these results from actualexperiments, the effect of body thickness is present and yet the resultsstill show that the basic principles of separation of key volumes arevalid. Note that there is a region of separation between corridors thathouse the different object classes. It is this fact which is used inconjunction with neural networks, as described in the above referencedpatent applications, which permit the design of a system that providesan accurate discrimination of rear facing child seats from forwardfacing humans. Heretofore before the techniques for locating thetransducers to separate these two zones were discovered, the entirediscrimination task was accomplished using neural networks. There wassignificant overlap between the reflections from the various objects andtherefore separation was done based on patterns of the reflected waves.By using the technology described herein to carefully orient thetransducers so as to create this region of separation of the criticalsurfaces, wherein all of the rear facing child seat data falls within aknown corridor, the task remaining for the neural networks issubstantially simplified with the result that the accuracy ofidentification is substantially improved.

[0166] Three general classes of child seats exist as well as severalmodels which are unique. First, there is the infant only seat as shownin FIG. 5 which is for occupants weighing up to about 20 pounds. This isdesigned to be only placed in the rear facing position. The second whichis illustrated in FIGS. 2 and 3 is for children from about 20 to about40 pounds and can be used in both the forward and rear facing positionand the third is for use only in the forward facing position and is forchildren weighing over about 40 pounds. All of these seats as well asthe unique models are used in test setups according to this inventionfor adapting a system to a vehicle. For each child seat, there areseveral hundred unique orientations representing virtually everypossible position of that seat within the vehicle. Tests are run, forexample, with the seat tilted 22 degrees, rotated 17 degrees, placed onthe front of the seat with the seat back fully up with the seat fullyback and with the window open as well as all variations of thereparameters. A large number of cases are also run, when practicing theteachings of this invention, with various accessories, such as clothing,toys, bottles, blankets etc., added to the child seat.

[0167] Similarly, wide variations are used for the occupants includingsize, clothing and activities such as reading maps or newspapers,leaning forward to adjust the radio, for example. Also included arecases where the occupant puts his/her feet on the dashboard or otherwiseassumes a wide variety of unusual positions. When all of the aboveconfigurations are considered along with many others not mentioned, thetotal number of configurations which are used to train the patternrecognition system can exceed 500,000. The goal is to include in theconfiguration training set representations of all occupancy states thatoccur in actual use. Since the system is highly accurate in making thecorrect decision for cases which are similar to those in the trainingset, the total system accuracy increases as the size of the training setincreases providing the cases are all distinct and not copies of othercases.

[0168] In addition to all of the variations in occupancy states, it isimportant to consider environmental effects during the data collection.Thermal gradients or thermal instabilities are particularly importantsince sound waves can be significantly diffracted by density changes inair. There are two aspects of the use of thermal gradients orinstability in training. First, the fact that thermal instabilitiesexist and therefore data with thermal instabilities present should bepart of database. For this case, a rather small amount of data collectedwith thermal instabilities would be used. A much more important use ofthermal instability comes from the fact that they add variability todata. Thus, considerably more data is taken with thermal instability andin fact, in some cases almost the entire database is taken with timevarying thermal gradients in order to provide variability to the data sothat the neural network does not memorize but instead generalizes fromthe data. This is accomplished by taking the data with a cold vehiclewith the heater operating and with a hot vehicle with the airconditioner operating. Additional data is also taken with a heat lamp ina closed vehicle to simulate a stable thermal gradient caused by sunloading.

[0169] To collect data for 500,000 vehicle configurations is not aformidable task. A trained technician crew can typically collect data onin excess on 2000 configurations or vectors per hour. The data iscollected typically every 50 to 100 milliseconds. During this time, theoccupant is continuously moving, assuming a continuously varyingposition and posture in the vehicle including moving from side to side,forward and back, twisting his/her head, reading newspapers and books,moving hands, arms, feet and legs, until the desired number of differentseated state examples are obtained. In some cases, this process ispracticed by confining the motion of an occupant into a particular zone.In some cases, for example, the occupant is trained to exercise thesedifferent seated state motions while remaining in a particular zone thatmay be the safe zone, the keep out zone, or an intermediate gray zone.In this manner, data is collected representing the airbag disable,depowered airbag enabled or full power airbag enabled states, in othercases, the actual position of the back of the head and/or the shouldersof the occupant are tracked using string pots, high frequency ultrasonictransducers, or optically. In this manner, the position of the occupantcan be measured and the decision as to whether this should be a disableor enable airbag case can be decided later. By continuously monitoringthe occupant, an added advantage results in that the data can becollected to permit a comparison of the occupant from one seated stateto another. This is particularly valuable in attempting to project thefuture location of an occupant based on a series of past locations aswould be desirable for example to predict when an occupant would crossinto the keep out zone during a panic braking situation prior to crash.

[0170] It is important to note that it is not necessary to train onevery vehicle produced but rather to train on each platform. A platformis an automobile manufacturer's designation of a group of vehicle modelsthat are built on the same vehicle structure.

[0171] A review of the literature on neural networks yields theconclusion that the use of such a large training set is unique in theneural network field. The rule of neural networks is that there must beat least three training cases for each network weight. Thus, forexample, if a neural network has 156 input nodes, 10 first hidden layernodes, 5 second hidden layer nodes, and one output node this results ina total of 1,622 weights. According to conventional theory 5000 trainingexamples should be sufficient. It is highly unexpected, therefore, thatgreater accuracy would be achieved through 100 times that many cases. Itis thus not obvious and cannot be deduced from the neural networkliterature that the accuracy of the system will improve substantially asthe size of the training database increases even to tens of thousands ofcases. It is also not obvious looking at the plots of the vectorsobtained using ultrasonic transducers that increasing the number oftests or the database size will have such a significant effect on thesystem accuracy. Each of the vectors is a rather course plot with a fewsignificant peaks and valleys. Since the spatial resolution of thesystem is typically about 3 to 4 inches, it is once again surprisingthat such a large database is required to achieve significant accuracyimprovements.

PROCESS FOR TRAINING A VEHICLE

[0172] The process for adapting an ultrasonic system to a vehicle willnow be described. A more detailed list of steps is provided in Appendix3. Although the pure ultrasonic system is described here, a similar setof steps applies when other technologies such as weight and opticalsystems are used. This description is thus provided to be exemplary andnot limiting:

[0173] 1. Select transducer and horn designs to fit the vehicle. At thisstage, usually full horns are used which are mounted so that theyproject into the passenger compartment. No attempt is made at this timeto achieve an esthetic matching of the transducers to the vehiclesurfaces. An estimate of the desired transducer fields are made at thistime either from measurements in the vehicle directly or from CADdrawings.

[0174] 2. Make polar plots of the transducer sonic fields. Transducersand candidate horns are assembled and tested to confirm that the desiredfield angles have been achieved. This frequently requires someadjustment of the transducers in the horn.

[0175] 3. Check to see that the fields cover the required volumes of thevehicle passenger compartment and do not impinge on adjacent flatsurfaces that may cause multipath effects. Redesign horns if necessary.

[0176] 4. Install transducers into vehicle.

[0177] 5. Map transducer fields in the vehicle and check for multipatheffects and proper coverage.

[0178] 6. Adjust transducer aim and re-map fields if necessary.

[0179] 7. Install daily calibration fixture and take standard setupdata.

[0180] 8. Acquire 50,000 to 100,000 vectors

[0181] 9. Adjust vectors for volume considerations by removing someinitial data points if cross talk is present and some final points tokeep data in the desired passenger compartment volume.

[0182] 10. Normalize vectors.

[0183] 11. Run neural network algorithm generating software to createalgorithm for vehicle installation.

[0184] 12. Check the accuracy of the algorithm. If not sufficientlyaccurate collect more data where necessary and retrain. If still notsufficiently accurate, add additional transducers to cover holes.

[0185] 13. When sufficient accuracy is attained, proceed to collect500,000 training vectors varying:

[0186] Occupancy (see Appendices 1 and 3):

[0187] Occupant size, position (zones), clothing etc

[0188] Child seat type, size, position etc.

[0189] Empty seat

[0190] Vehicle configuration:

[0191] Seat position

[0192] Window position

[0193] Visor and armrest position

[0194] Presence of other occupants in adjoining seat or rear seat

[0195] Temperature

[0196] Temperature gradient—stable

[0197] Temperature turbulence—heater and air conditioner

[0198] Wind turbulence—High speed travel with windows open, top down etc

[0199] 14. Collect ˜100,000 vectors of Independent data using othercombinations of the above

[0200] 15. Collect ˜50,000 vectors of “real world data” to represent theacceptance criteria and more closely represent the actual seated stateprobabilities in the real world.

[0201] 16. Train network and create algorithm using the training vectorsand the Independent data vectors.

[0202] 17. Validate the algorithm using the real world vectors.

[0203] 18. Install algorithm into the vehicle and test.

[0204] 19. Decide on post processing methodology to remove final holesin system

[0205] 20. Implement post-processing methods into the algorithm

[0206] 21. Final test. The process up until step 13 involve the use oftransducers with full horns mounted on the surfaces of the interiorpassenger compartment. At some point, the actual transducers which areto be used in the final vehicle must be substituted for the trialtransducers. This is either done prior to step 13 or at this step. Thisprocess involves designing transducer holders that blend with the visualsurfaces of the passenger compartment so that they can be covered with ascreen or mesh to retain the esthetic quality of the interior. This isusually a lengthy process and involves several consultations with thecustomer. Usually, therefore, the steps from 13 through 20 are repeatedat this point after the final transducer and holder design has beenselected. The initial data taken with fall horns gives a measure of thebest system that can be made to operate in the vehicle. Some degradationin performance is expected when the esthetic horns are substituted forthe full horns. conducting two complete data collection cycles anaccurate measure of this accuracy reduction can be obtained.

[0207] 22. Ship to customers to be used in production vehicles.

[0208] 23. Collect additional real world validation data for continuousimprovement.

[0209] More detail on the operation of the transducers and controlcircuitry as well as the neural network is provided in the abovereferenced patents and patent applications and is included herein as ifthe entire text of the same were reproduced here. One particular exampleof a successful neural network for the two transducer case had 78 inputnodes, 6 hidden nodes and one output node and for the four transducercase had 176 input nodes 20 hidden layer nodes on hidden layer one, 7hidden layer nodes on hidden layer 2 and one output node. The weights ofthe network were determined by supervised training using the backpropagation method as described in the referenced patent applicationsand in more detail in the references cited therein. Naturally otherneural network architectures are possible including RCE, LogiconProjection, Stochastic etc.

[0210] Finally, the system is trained and tested with situationsrepresentative of the manufacturing and installation tolerances thatoccur during the production and delivery of the vehicle as well as usageand deterioration effects. Thus, for example, the system is tested withthe transducer mounting positions shifted by up to one inch in anydirection and rotated by up to 15 degrees, with a simulated accumulationof dirt and other variations. This tolerance to vehicle variation alsosometimes permits the installation of the system onto a different butsimilar model vehicle with, in many cases, only minimal retraining ofthe system.

[0211] The speed of sound varies with temperature, humidity, andpressure. This can be compensated for by using the fact that thegeometry between the transducers is known and the speed of sound cantherefore be measured. Thus, on vehicle startup and as often as desiredthereafter, the speed of sound can be measured by one transducer, suchas transducer 110 in FIG. 5, sending a signal which is directly receivedby another transducer. Since the distance separating them is known, thespeed of sound can be calculated and the system automatically adjustedto remove the variation due to the change in the speed of sound.Therefore, the system operates with same accuracy regardless of thetemperature, humidity or atmospheric pressure. It may even be possibleto use this technique to also automatically compensate for any effectsdue to wind velocity through an open window. An additional benefit ofthis system is that it can be used to determine the vehicle interiortemperature for use by other control systems within the vehicle sincethe variation in the velocity of sound is a strong function oftemperature and a weak function of pressure and humidity.

[0212] The problem with the speed of sound measurement described aboveis that some object in the vehicle may block the path from onetransducer to another. This of course could be checked and a correctionnot be made if the signal from one transducer does not reach the othertransducer. The problem, however, is that the path might not becompletely blocked but only slightly blocked. This would cause theultrasonic path length to increase, which would give a false indicationof a temperature change. This can be solved by using more than onetransducer. All of the transducers can broadcast signals to all of theother transducers. The problem here, of course, is which transducer pairdoes one believe if they all give different answers. The answer is theone that gives the shortest distance or the greatest calculated speed ofsound. By this method, there are a total of 6 separate paths for fourultrasonic transducers.

[0213] An alternative method of determining the temperature is to usethe transducer circuit to measure some parameter of the transducer thatchanges with temperature. For example the natural frequency ofultrasonic transducers changes in a known manner with temperature andtherefore by measuring the natural frequency of the transducer thetemperature can be determined. Since this method does not requirecommunication between transducers, it would also work in situationswhere each transducer has a different resonant frequency.

[0214] The process by which all of the distances are carefully measuredfrom each transducer to the other transducers and the algorithmdeveloped to determine the speed of sound, is a significant part of theteachings of the instant invention. Prior to this, the speed of soundcalculation was based on a single transmission from one transducer to aknown second transducer. This resulted in an inaccurate system designand degraded the accuracy of systems in the field.

[0215] Another important feature of a system, developed in accordancewith the teachings of this invention, is the realization that motion ofthe vehicle can be used in a novel manner to substantially increase theaccuracy of the system. Ultrasonic waves reflect on most objects aslight off a mirror. This is due to the relatively long wavelength ofultrasound as compared with light. As a result, certain reflections canoverwhelm the receiver and reduce the available information. Whenreadings are taken while the occupant and/or the vehicle is in motion,and these readings averaged over several transmission/reception cycles,the motion of the occupant and vehicle causes various surfaces to changetheir angular orientation slightly but enough to change the reflectivepattern and reduce this mirror effect. The net effect is that theaverage of several cycles gives a much clearer image of the reflectingobject than is obtainable from a single cycle. This then provides abetter image to the neural network and significantly improves theidentification accuracy of the system. The choice of the number ofcycles to be averaged depends on the system requirements. For example,if dynamic out-of-position is required then each vector must be usedalone and averaging in the simple sense cannot be used. This will bediscussed more detail below.

[0216] When an occupant is sitting in the vehicle during normal vehicleoperation, the determination of the occupancy state can be substantiallyimproved by using successive observations over a period of time.

[0217] This can either be accomplished by averaging the data prior toinsertion into a neural network, or alternately the decision of theneural network can be averaged. This is known as the categorizationphase of the process. During categorization the occupancy state of thevehicle is determined. Is the vehicle occupied by the forward facinghuman, an empty seat, a rear facing child seat, or an out-of-positionhuman? Typically many seconds of data can be accumulated to make thecategorization decision.

[0218] When a driver senses an impending crash, on the other hand, he orshe will typically slam on the brakes to try to slow vehicle prior toimpact. If an occupant is unbelted, he or she will begin moving towardthe airbag during this panic braking. For the purposes of determiningthe position of the occupant, there is not sufficient time to averagedata as in the case of categorization. Nevertheless, there isinformation in data from previous vectors that can be used to partiallycorrect errors in current vectors, which may be caused by thermaleffects, for example. One method is to determine the location of theoccupant using the neural network based on previous training. The motionof the occupant can then be compared to a maximum likelihood positionbased on the position estimate of the occupant at previous vectors.Thus, for example, perhaps the existence of thermal gradients in thevehicle caused an error in the current vector leading to a calculationthat the occupant has moved 12 inches since the previous vector. Sincethis could be a physically impossible move during ten milliseconds, themeasured position of the occupant can be corrected based on his previouspositions and known velocity. Naturally, if an accelerometer is presentin the vehicle and if the acceleration data is available for thiscalculation, a much higher accuracy prediction can be made. Thus, thereis information in the data in previous vectors as well as in thepositions of the occupant determined from the this data that can be usedto correct erroneous data in the current vector and, therefore, in amanner not too dissimilar from the averaging method for categorization,the position accuracy of the occupant can be known with higher accuracy.

[0219] Returning to the placement of ultrasonic transducers for theultrasonic occupant position sensor system, as to the more novelfeatures of the invention for the placement of ultrasonic transducers,this application discloses (1) the application of two sensors tosingle-axis monitoring of target volumes; (2) the method of locating twosensors spanning a target volume to sense object positions, that is,transducers are mounted along the sensing axis beyond the objects to besensed; (3) the method of orientation of the sensor axis for optimaltarget discrimination parallel to the axis of separation ofdistinguishing target features; and (4) the method of defining the headand shoulders and supporting surfaces as defining humans for rear facingchild seat detection and forward facing human detection.

[0220] Considerable work is ongoing to improve the resolution of theultrasonic transducers. To take advantage of higher resolutiontransducers, more closer together data points should be obtained. Thismeans that after the envelope has been extracted from the returnedsignals, the sampling rate should be increased from approximately 1000samples per second to perhaps 2000 samples per second or even higher. Bydoubling or tripling the amount data required to be analyzed, the systemwhich is mounted on the vehicle will require greater computationalpower. This results in a more expensive electronic system. Not all ofthe data is of equal importance, however. The position of the occupantin the normal seating position does not need to be known with greataccuracy whereas as that occupant is moving toward the keep out zoneboundary during pre-crash braking, the spatial accuracy requirementsbecome more important. Fortunately, the neural network algorithmgenerating system has the capability of indicating to the systemdesigner the relative value of each of the data points used by theneural network. Thus, as many as, for example, 500 data points pervector may be collected and fed to the neural network during thetraining stage and, after careful pruning, the final number of datapoints to be used by the vehicle mounted system may be reduced to 150,for example. This technique of using the neural networkalgorithm-generating program to prune the input data is an importantteaching of the present invention. By this method, the advantages ofhigher resolution transducers can be optimally used without increasingthe cost of the electronic vehicle mounted circuits. Also, once theneural network has determined the spacing of the data points, this canbe fine-tuned, for example, by acquiring more data points at the edge ofthe keep out zone as compared to positions well into the safe zone. Theinitial technique is done be collecting the full 500 data points, forexample, while in the system installed in the vehicle the datadigitization spacing can be determined by hardware or software so thatonly the required data is acquired.

[0221] The technique that was described above for the determination ofthe location of an occupant during panic or braking pre-crash situationsinvolved the use of a modular neural network. In that case, one neuralnetwork was used to determine the occupancy state of the vehicle and thesecond neural network was used to determine the location of the occupantwithin the vehicle. The method of designing a system utilizing multipleneural networks is a key teaching of the present invention. When thisidea is generalized, many potential combinations of multiple neuralnetwork architectures become possible. Some of these will now bediscussed.

[0222] One of the earliest attempts to use multiple neural networks wasto combine different networks trained differently but on substantiallythe same data under the theory that the errors which affect the accuracyof one network would be independent of the errors which affect theaccuracy of another network. For example, for a system containing fourultrasonic transducers, four neural networks could be trained each usinga different subset of the four transducer data. Thus, if the transducersare arbitrarily labeled A, B, C and D the then the first neural networkwould be trained on data from A, B and C. The second neural networkwould be trained on data from B, C, and D etc. This technique has notmet with a significant success since it is an attempt to mask errors inthe data rather than to eliminate them. Nevertheless, such a system doesperform marginally better in some situations compared to a singlenetwork using data from all four transducers. The penalty for using sucha system is that the computational time is increased by approximately afactor of three. This significantly affects the cost of the systeminstalled in a vehicle.

[0223] An alternate method of obtaining some of the advantages of theparallel neural network architecture described above, is to form asingle neural network but where the nodes of one or more of the hiddenlayers are not all connected to all of the input nodes. Alternately, ifthe second hidden layer is chosen, all of the notes from the previoushidden layer are not connected to all of the nodes of the subsequentlayer. The alternate groups of hidden layer nodes can then feed todifferent output notes and the results of the output nodes combined,either through a neural network training process into a single decisionor a voting process. This latter approach retains most of the advantagesof the parallel neural network while substantially reducing thecomputational complexity.

[0224] The fundamental problem with parallel networks is that they focuson achieving reliability or accuracy by redundancy rather than byimproving the neural network architecture itself or the quality of thedata being used. They also increase the cost of the final vehicleinstalled systems. Alternately, modular neural networks improve theaccuracy of the system by dividing up the tasks. For example, if asystem is to be designed to determine the type of tree and the type ofanimal in a particular scene, the modular approach would be to firstdetermine whether the object of interest is an animal or a tree and thenuse separate neural networks to determine type of tree and the type ofanimal. When a human looks at a tree he is not ask himself is that atiger or a monkey. Modular neural network systems are efficient sinceonce the categorization decision is made, the seat is occupied byforward facing human, for example, the location of that object can bedetermined more accurately and without requiring increased computationalresources.

[0225] Another example where modular neural networks have provenvaluable is provide a means for separating “normal” from “specialcases”. It has been found that in some cases, the vast majority of thedata falls into what might be termed “normal” cases that are easilyidentified with a neural network. The balance of the cases cause theneural network considerable difficulty, however, there are identifiablecharacteristics of the special cases that permits them to be separatedfrom the normal cases and dealt with separately. Various types of humanintelligence rules can be used, in addition to a neural network, toperform this separation including fuzzy logic, statistical filteringusing the average class vector of normal cases, the vector standarddeviation, and threshold where a fuzzy logic network is used todetermine chance of a vector belonging to a certain class. If the chanceis below a threshold, the standard neural network is used and if abovethe special one is used.

[0226] Mean-Variance connections, Fuzzy Logic, Stochastic, and GeneticAlgorithm networks, and combinations thereof such as Neuro-Fuzzy systemsare other technologies considered. During the process of designing asystem to be adapted to a particular vehicle, many different neuralnetwork architectures are considered including those mentioned above.The particular choice of architecture is frequently determined on atrial and error basis by the system designer. Although the parallelarchitecture system described above has not proven to be in generalbeneficial, one version of this architecture has shown some promise. Itis known that when training a neural network, that as the trainingprocess proceeds the accuracy of the decision process improves for thetraining and independent databases. It is also known that the ability ofthe network to generalize suffers. That is, when the network ispresented with a system which is similar to some case in the databasebut still with some significant differences, the network may make theproper decision in the early stages of training, but the wrong decisionsafter the network has become fully trained. This is sometimes called theyoung network vs. old network dilemma. In some cases, therefore, usingan old network in parallel with a young network can retain some of theadvantages of both networks, that is, the high accuracy of the oldnetwork coupled with the greater generality of the young network. Onceagain, the choice of any of these particular techniques is part of theprocess of designing a system to be adapted to a particular vehicle andis the prime subject of this invention. The particular combination oftools used depends on the particular application and the experience ofthe system designer.

[0227] The methods above have been described in connection with the useof ultrasonic transducers. Many of the methods, however, are alsoapplicable to optical, radar and other sensing systems and whereapplicable, this invention is not limited to ultrasonic systems. Inparticular, an important feature of this invention is the properplacement of three or more separately located receivers such that thesystem still operates with high reliability if one of the receivers isblocked by some object such as a newspaper. This feature is alsoapplicable to systems using electromagnetic radiation instead ofultrasonic, however the particular locations will differ based on theproperties of the particular transducers. Optical sensors based ontwo-dimensional cameras or other image sensors, for example, are moreappropriately placed on the sides of a rectangle surrounding the seat tobe monitored rather than at the comers of such a rectangle as is thecase with ultrasonic sensors. This is because ultrasonic sensors measurean axial distance from the sensor where the camera is most appropriatefor measuring distances up and down and across its field view ratherthan distances to the object. With the use of electromagnetic radiationand the advances which have recently been made in the field of very lowlight level sensitivity, it is now possible, in some implementations, toeliminate the transmitters and use background light as the source ofillumination along with using a technique such as auto-focusing toobtain the distance from the receiver to the object. Thus, onlyreceivers would be required further reducing the complexity of thesystem.

[0228] Although implicit in the above discussion, an important featureof this invention which should be emphasized is the method of developinga system having distributed transducer mountings. Other systems whichhave attempted to solve the rear facing child seat (RFCS) andout-of-position problems have relied on a single transducer mountinglocation or at most, two transducer mounting locations. Such systems canbe easily blinded by a newspaper or by the hand of an occupant, forexample, which is imposed between the occupant and the transducers. Thisproblem is almost completely eliminated through the use of three or moretransducers which are mounted so that they have distinctly differentviews of the passenger compartment volume of interest. If the system isadapted using four transducers as illustrated in the distributed systemof FIG. 9, for example, the system suffers only a slight reduction inaccuracy even if two of the transducers are covered so as to make theminoperable.

[0229] It is important in order to obtain the full advantages of thesystem when a transducer is blocked, that the training and independentdatabases contains many examples of blocked transducers. If the patternrecognition system, the neural network in this case, has not beentrained on a substantial number of blocked transducer cases, it will notdo a good job in recognizing such cases later. This is yet anotherinstance where the makeup of the databases is crucial to the success ofdesigning the system that will perform with high reliability in avehicle and is an important aspect of the instant invention.

[0230] Other techniques which may or may not be part of the process ofdesigning a system for a particular application include the following:

[0231] 1. Fuzzy logic. Neural networks frequently exhibit the propertythat when presented with a situation that is totally different from anypreviously encounter, an irrational decision can result. Frequently whenthe trained observer looks at input data, certain boundaries to the databecome evident and cases that fall outside of those boundaries areindicative of either corrupted data or data from a totally unexpectedsituation. It is sometimes desirable for the system designer to addrules to handle these cases. These can be fuzzy logic based rules orrules based on human intelligence. One example would be that whencertain parts of the data vector fall outside of expected bounds thatthe system defaults to an airbag enable state.

[0232] 2. Genetic algorithms. When developing a neural network algorithmfor a particular vehicle, there is no guarantee that the best of allpossible algorithms has been selected. One method of improving theprobability that the best algorithm has been selected is to incorporatesome of the principles of genetic algorithms. In one application of thistheory, the network architecture and/or the node weights are variedpseudo-randomly to attempt to find other combinations which have highersuccess rates. The discussion of such genetic algorithms systems appearsin the book Computational Intelligence referenced above.

[0233] 3. Pre-processing. For military target recognition is common touse the Fourier transform of the data rather than the data itself Thiscan be especially valuable for categorization as opposed to location ofthe occupant and the vehicle. When used with a modular network, forexample, the Fourier transform of the data may be used for thecategorization neural network and the non-transformed data used for theposition determination neural network. Recently wavelet transforms havealso been considered as a preprocessor.

[0234] 4. Occupant position determination comparison. Above, under thesubject of dynamic out-of-position, it was discussed that the positionof the occupant can be used as a filter to determine the quality of thedata in a particular vector. This technique can also be used in generalas a method to improve the quality of a vector of data based on theprevious positions of the occupant. This technique can also be expandedto help differentiate live objects in the vehicle from inanimateobjects. For example, a forward facing human will change his positionfrequently during the travel of the vehicle whereas a box will tend toshow considerably less motion. This is also useful, for example, indifferentiating a small human from an empty seat. The motion of a seatcontaining a small human will be significantly different from that of anempty seat even though the particular vector may not show significantdifferences. That is, a vector formed from the differences from twosuccessive vectors is indicative of motion and thus of an occupant.

[0235] 5. Blocked transducers. It is sometimes desirable to positivelyidentify a blocked transducer and when such a situation is found to usea different neural network which has only been trained on the subset ofunblocked transducers. Such a network, since it has been trainedspecifically on three transducers, for example, will generally performmore accurately than a network which has been trained on fourtransducers with one of the transducers blocked some of the time. Once ablocked transducer has been identified the occupant can be notified ifthe condition persists for more than a reasonable time.

[0236] 6. Other Basic Architectures. The back propagation neural networkis a very successful general-purpose network. However, for someapplications, there are other neural network architectures that canperform better. If it has been found, for example, that a parallelnetwork as described above results in a significant improvement in thesystem, then, it is likely that the particular neural networkarchitecture chosen has not been successful in retrieving all of theinformation that is present in the data. In such a case an RCE,Stochastic, Logicon Projection, or one of the other approximately 30types of neural network architectures can be tried to see if the resultsimprove. This parallel network test, therefore, is a valuable tool fordetermining the degree to which the current neural network is capable ofusing efficiently the available data.

[0237] 7. Transducer Geometry. Another technique, which is frequentlyused in designing a system for a particular vehicle, is to use a neuralnetwork to determine the optimum mounting locations, aiming directionsand field angles of transducers. For particularly difficult vehicles itis sometimes desirable to mount a large number of ultrasonictransducers, for example, and then use the neural network to eliminatethose transducers which are least significant. This is similar to thetechnique described above where all kinds of transducers are combinedinitially and later pruned.

[0238] 8. Data quantity. Since it is very easy to take large amountsdata and yet large databases require considerably longer training timefor a neural network, a test of the variability of the database can bemade using a neural network. If for example after removing half of thedata in the database, the performance of a trained neural networkagainst the validation database does not decrease, then the systemdesigner suspects that the training database contains a large amount ofredundant data. Techniques such as similarity analysis can then be usedto remove data that is virtually indistinguishable from other data.Since it is important to have a varied database, it is undesirablegenerally to have duplicate or essentially duplicate vectors in thedatabase since the presence of such vectors can bias system and drivethe system more toward memorization and away from generalization.

[0239] 9. Environmental factors. An evaluation can be made of thebeneficial effects of using varying environmental influences during datacollection on the accuracy of the system using neural networks alongwith a technique such as design of experiments.

[0240] 10. Database makeup. It is generally believed that the trainingdatabase must be flat meaning that all of the occupancy states that theneural network must recognize must be approximately equally representedin the training database. Typically, the independent database hasapproximately the same makeup as the training database. The validationdatabase, on the other hand, typically is represented in a non-flatbasis with representative cases from real world experience. Since thereis no need for the validation database to be flat, it can include manyof the extreme cases as well as being highly biased towards the mostcommon cases. This is the theory that is currently being used todetermine the makeup of the various databases. The success of thistheory continues to be challenged by the addition of new cases to thevalidation database. When significant failures are discovered in thevalidation database, the training and independent databases are modifiedin an attempt to remove the failure.

[0241] 11. Biasing. All seated state occupancy states are not equallyimportant. The final system must be nearly 100% accurate for forwardfacing in-position humans. Since that will comprise the majority of thereal world situations, even a small loss in accuracy here will cause theairbag to be disabled in a situation where it otherwise would beavailable to protect an occupant. A small decrease in accuracy will thusresult in a large increase in deaths and injuries. On the other hand,there are no serious consequences if the airbag is deployed occasionallywhen the seat is empty. Various techniques are used to bias the data inthe database to take this into account. One technique is to give a muchhigher value to the presence of a forward facing human during thesupervised learning process than to an empty seat. Another technique isto include more data for forward facing humans than for empty seats.This, however, can be dangerous as an unbalanced network leads to a lossof generality.

[0242] 12. Screening. It is important that the loop be closed on dataacquisition. That is, the data must be checked at the time the data isacquired to the sure that it is good data. Bad data can happen becauseof electrical disturbances on the power line, sources of ultrasound suchas nearby welding equipment, or due to human error. If the data remainsin the training database, for example, then it will degrade theperformance of the network. Several methods exist for eliminating baddata. The most successful method is to take an initial quantity of data,such as 30,000 to 50,000 vectors, and create an interim network. This isnormally done anyway as an initial check on the system capabilitiesprior to engaging in an extensive data collection process. The networkcan be trained on this data and, as the real training data is acquired,the data can be tested against the neural network created on the initialdata set. Any vectors that fail are examined for reasonableness.

[0243] 13. Vector normalization method. Through extensive research ithas been found that the vector should be normalized based on all of thedata in the vector, that is have all its data values range from 0 to 1.For particular cases, however, it has been fond desirable to apply thenormalization process selectively, eliminating or treating differentlythe data at the early part of the data from each transducer. This isespecially the case when there is significant ringing on the transduceror cross talk when a separate send and receive transducer is used. Thereare times when other vector normalization techniques are required andthe neural network system can be used to determine the best vectornormalization technique for a particular application.

[0244] 14. Feature extraction. The success of a neural network systemcan frequently be aided if additional data is inputted into the network.One example can be the number of 0 data points before the first peak isexperience. Alternately, the exact distance to the first peak can bedetermined prior to the sampling of the data. Other features can includethe number of peaks, the distance between the peaks, the width of thelargest peak, the normalization factor, the vector mean or standarddeviation, etc. These normalization techniques are frequently used atthe end of the adaptation process to slightly increase the accuracy ofthe system.

[0245] 15. Noise. It has been frequently reported in the literature thatadding noise to the data that is provided to a neural network canimprove the neural network accuracy by leading to better generalizationand away from memorization. However, the training of the network in thepresence of thermal gradients has been shown to substantially eliminatethe need to artificially add noise to the data. Nevertheless, in somecases, improvements have been observed when random arbitrary noise of arather low level is superimposed on the training data.

[0246] 16. Photographic recording of the setup. After all of the datahas been collected and used to train a neural network, it is common tofind a significant number of vectors which, when analyzed by the neuralnetwork, give in a weak or wrong decision. These vectors must becarefully studied especially in comparison with adjacent vectors to seeif there is an identifiable cause for the weak or wrong decision.Perhaps the occupant was on the borderline of the keep out zone andstrayed into the keep out zone during a particular data collectionevent. For this reason is desirable to photograph each setupsimultaneous with the collection of the data. This can be done using acamera mounted in a position whereby it obtains a good view of the seatoccupancy. Sometimes several cameras are necessary to minimize theeffects of blockage by a newspaper, for example. Having the photographicrecord of the data setup is also useful when similar results areobtained when the vehicle is subjected to road testing. During roadtesting, the camera is also present and the test engineer is required toinitiate data collection whenever the system does not provide thecorrect response. The vector and the photograph of this real world testcan later be compared to similar setups in the laboratory to see whetherthere is data that was missed in deriving the matrix of vehicle setupsfor training the vehicle.

[0247] 17. Automation. When collecting data in the vehicle it isdesirable to automate the motion of the vehicle seat, seatback, windows,visors etc. in this manner the positions of these items can becontrolled and distributed as desired by the system designer. Thisminimizes the possibility of taking too much data at one configurationand thereby unbalancing the network.

[0248] 18. Automatic setup parameter recording. To achieve an accuratedata set, the key parameters of the setup should be recordedautomatically. These include the temperatures at various positionsinside the vehicle, the position of the vehicle seat, and seatback, theposition of the headrest, visor and windows and, where possible, theposition of the vehicle occupants. The automatic recordation of theseparameters minimizes the effects of human errors.

[0249] 19. Laser Pointers. During the initial data collection with fullhorns mounted on the surface of the passenger compartment, care must theexercised so that the transducers are not accidentally moved during thedata collection process. In order to check for this possibility, a smalllaser diode is incorporated into each transducer holder. The laser isaimed so that it illuminates some other surface of the passengercompartment at a known location. Prior to each data taking session, eachof the transducer aiming points is checked.

[0250] 20. Multi-frequency transducer placement. When data is collectedfor dynamic out-of-position, each of the ultrasonic transducers mustoperate at the different frequency so that all transducers can transmitsimultaneously. By this method data can be collected every 10milliseconds, which is sufficiently fast to approximately track themotion of an occupant during pre-crash braking prior to an impact. Aproblem arises in the spacing of the frequencies between the differenttransducers. If the spacing is too close, it becomes very difficult toseparate the signals from different transducers and it also affects thesampling rate of the transducer data and thus the resolution of thetransducers. If an ultrasonic transducer operates module below 35 kHz itcan be sensed by dogs and other animals. If the transducer operates muchabove 70 kHz, it is very difficult to make the open type of ultrasonictransducer which produces the highest sound pressure. If the multiplefrequency system is used for both the driver and passenger-side, eightseparate frequencies are required. In order to find eight frequenciesbetween 35 and 70 kHz, a frequency spacing of 5 kHz is required. Inorder to use conventional electronic filters and to provide sufficientspacing to permit the desired resolution at the keep out zone border, a10 kHz spacing is desired. These incompatible requirements can be solvedthrough a careful judicious placement of the transducers such thattransducers that are within 5 kHz of each other are placed in such amanner that there is no direct path between the transducers and anyindirect path is sufficiently long so that it can be filteredtemporarily. An example of such an arrangement is shown on FIG. 11. Forthis example the transducers operate at the following frequencies A 65kHz, B 55 kHz, C 35 kHz, D 45 kHz, E 50 kHz, F 40 kHz, G 60 kHz, H 70kHz. Actually other arrangements adhering to the principle describedabove would also work.

[0251] 21. Use of a PC in data collection. When collecting data for thetraining, independent, and validation databases, it is frequentlydesirable to test the data using various screening techniques and todisplay the data on a monitor. Thus, during data collection the processis usually monitored using a desktop PC for data taken in the laboratoryand a laptop PC for data taken on the road.

[0252] 22. Use of referencing markers and gages. In addition to andsometimes as a substitution for, the automatic recording of thepositions of the seats, seatbacks, windows etc. as described above, avariety of visual markings and gages are frequently used. This includesmarkings to show the angular position of the seatback, the location ofthe seat on the seat track, the openness of the window, etc. Also inthose cases where automatic tracking of the occupant is not implemented,visual markings are placed such that a technician can observe that thetest occupant remains within the required zone for the particular datataking exercise. Sometimes, a laser diode is used to create a visualline in the space that represents the boundary of the keep out zone orother desired zone boundary.

[0253] It is important to realize to the adaptation process describedherein applies to any combination of transducers that provideinformation about the vehicle occupancy. These include weight sensors,capacitive sensors, inductive sensors, moisture sensors, ultrasonic,optic, infrared, radar among others. The adaptation process begins witha selection of candidate transducers for a particular vehicle model.This selection is based on such considerations as cost, alternate usesof the system other than occupant sensing, vehicle interior passengercompartment geometry, desired accuracy and reliability, vehicleaesthetics, vehicle manufacturer preferences, and others. Once acandidate set of transducers has been chosen, these transducers aremounted in the test vehicle according to the teachings of thisinvention. The vehicle is then subjected to an extensive data collectionprocess wherein various objects are placed in the vehicle that variouslocations as described below and an initial data set is collected. Apattern recognition system is then developed using the acquired data andan accuracy assessment is made. Further studies are made to determinewhich if any of the transducers can be eliminated from the design. Ingeneral the design process begins with a surplus of sensors plus anobjective as to how many sensors are to be in the final vehicleinstallation. The adaptation process can determine which of thetransducers are most important and which are least important and theleast important transducers can be eliminated to reduce system cost andcomplexity.

[0254] Although several preferred methods are illustrated and describedabove, there are other possible combinations using different sensorslocated at different positions within the automobile passengercompartment which measure either the same or different characteristicsof an occupying object to accomplish the same or similar goals as thosedescribed herein. There are also numerous additional applications inaddition to those described above including, but not limited to,monitoring the driver seat, the center seat or the rear seat of thevehicle or for controlling other vehicle systems in addition to theairbag system. This invention is not limited to the above embodimentsand should be determined by the following claims.

Appendix 1

[0255] Subject Classification Class Instances Weight Category State ESEmpty Seat <10 lb Empty FFA Normally Seated Adult >105 lb Enable FFCNormally Seated Child <10,105> lb Enable FFC Normally Positioned ForwardFacing <10,45> lb Enable Child Seat OOP Out-of-position Adult >105 lbDisable OOP Out-of-position Child <105 lb Disable OOP Out-of-positionForward Facing <10,45> lb Disable Child Seat RFS Rearward Facing ChildSeat <10,45> lb Disable RFS Rearward Facing Infant Seat <10,45> lbDisable

[0256] Weight Range Height Range kg (lb) m (in) Categorization of HumanSubjects Child <0.95, 1.15> (<3′1″, 3′9″>) <1.10, 1.30> (<3′7″, 4′3″>) <1.25, 1.45> (<4′1″, 4′9″>) <11, 25> (<24, 55>) C11 C12 C13 <22, 36>(<48, 79>) C21 C22 C23  <33, 47> (<73, 103>) C31 C32 C33 Adult <1.45,1.65> (<4′9″, 5′5″>) <1.60, 1.80> (<5′3″, 5′11″>) <1.75, 1.95> (<5′9″,6′5″>)  <45, 70> (<99, 154>) A11 A12 A13  <65, 90> (<143, 198>) A21 A22A23  <85, 110> (<187, 242>) A31 A32 A33 All Human Subjects are to wearlight clothes (typically slacks and T-shirt) on entry. Other types ofclothing to be provided by ATI Child Surrogates Doll Baby = 0.50 m(approx. 20″) Infant = 0.75 m (approx. 30″) Child = 1.20 m (approx. 48″)

[0257] Rearward Facing Infant Seats Designation Child Seat AttributesTraining Arriva base, hood Independent Assura 565 hood TrainingBaby-Safe — Training Century 590 base, hood Training Evenflo Discoverybase, Tbar Training Evenflo Joyride (new) hood Independent EvenfloJoyride (old) — Training Gerry Guard base Validation KolcraftTravelabout base, Tbar Training Rock-n-Ride — Training TLC —

[0258] Rearward Facing Child Seat Designation Child Seat AttributesTraining Century 1000 — Validation Century 2000 STE — Training CenturyOvation — Training Century Smartmove 5T table Training Champion tableTraining Fisher Price Child Seat table Training Touriva — TrainingUltara table Training Vario Exclusive table

[0259] Forward Facing Child and Booster Seats Designation Child SeatAttributes Training Century 1000 — Validation Century 2000 STE —Training Century Ovation — Validation Century Smartmove 5T tableTraining Champion table Validation Fisher Price Booster — TrainingFisher Price Child Seat table Training Gerry Booster table TrainingTouriva — Training Ultara table Training Vario Exclusiv table

[0260] Vehicle Configuration Series Seat Track (+/−0.5″) SeatbackRecline (+/−2°) Windows Configuration 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 78 9 10 1 2 3 4 5 6 7 8 A 0 0 2 2 2 4 4 6 6 6 0 18 4 12 20 2 20 0 8 16 DD U U D D U U B 1 1 3 3 3 5 5 7 7 7 2 20 0 8 16 0 18 4 12 20 U U D D U UD D C 0 0 2 2 4 4 4 6 6 6 5 15 4 16 0 15 20 2 10 18 U U D D U U D D D 11 3 3 5 5 5 7 7 7 4 16 5 15 2 10 18 0 15 20 D D U U D D U U E 0 0 0 2 22 4 4 6 6 0 8 16 4 12 20 2 20 0 18 D D U U D D U U F 1 1 1 3 3 3 5 5 7 74 12 20 0 8 16 0 18 2 20 U U D D U U D D G 0 0 2 2 2 4 4 4 6 6 4 16 2 202 10 18 0 15 20 U U D D U U D D H 1 1 3 3 3 5 5 5 7 7 2 20 4 16 0 15 202 10 18 D D U U D D U U Windows Visor Convertible Top Configuration 9 101 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 A D D U D U D S U D U D S U UU U U D D D D D B U U D U D U S D U D U S D D D D D U U U U U C U U U DS U D U D S U D D D D D D U U U U U D D D D U S D U D U S D U U U U U UD D D D D E D D D U D S U D U S D U U U U U U D D D D D F U U U D U S DU D S U D D D D D D U U U U U G U U U S D U D U D U S D D D D D D U U UU U H D D D S U D U D U D S U U U U U U D D D D D # Apply small (+/−10°) rotations. # Sit back in the seat; “operate” radio controls, glovebox, window, or seat controls; assume a brace posture; Do not cross thefire line with head and/or shoulders at any time.

Network Training Set Collection Matrix (Vehicle E) Rev 1.1

[0261] # Class Subject/Object Attributes Actions Config. Belt Conditions 1 ES None None Motions of track and (A) N.A. Ambient recline  2 FFA A22Medium Clothes, Motions in safe seating B Yes Ambient Magazine area  3OOP A22 Medium Clothes Motions in NFZ C No Ambient  4 FFC Century 1000Infant Doll Motions in safe seating D No Ambient area  5 RFS Century1000 Baby Doll Motions in entire E No Ambient seating area  6 ES NoneBeaded Cover Motions of track and (F) N.A. Ambient recline  7 FFA AllMedium Clothes Motions in safe seating G Yes Ambient area  8 OOP TourivaInfant Doll, Blanket Motions in NFZ H No Ambient  9 FFC Touriva InfantDoll, Blanket Motions in safe seating A No Ambient area 10 RFS Century590 Baby Doll, Hood Motions in entire B No Ambient seating area 11 ESNone Fabric Cover Motions of track and (C) N.A. Ambient recline 12 FFAA33 Medium Clothes, Motions in safe seating D No Ambient Newspaper area13 OOP A33 Medium Clothes Motions in NFZ E Yes Ambient 14 FFC C22 MediumClothes Motions in safe seating F No Ambient area 15 RFS Touriva BabyDoll, Blanket Motions in entire G No Ambient seating area 16 ES NoneBlanket Motions of track and (H) N.A. Ambient recline 17 FFA A21 HeavyClothes Motions in safe seating A No Ambient area 18 OOP C11 HeavyClothes Motions in NFZ B No Ambient (standing) 19 FFC C11 Heavy ClothesMotions in safe seating C No Ambient area 20 RFS TLC Baby Doll Motionsin entire D No Ambient seating area 21 ES None None Motions of track and(E) N.A. Solar Heat recline 22 FFA A12 Light Clothes, Motions in safeseating F Yes Solar Heat Magazine area 23 OOP A12 Light Clothes Motionsin NFZ G No Solar Heat 24 FFC Champion Infant Doll Motions in safeseating H No Solar Heat area 25 RFS Champion Baby Doll Motions in entireA No Solar Heat seating area 26 ES None Beaded Cover Motions of trackand (B) N.A. Solar Heat recline 27 FFA A23 Light Clothes Motions in safeseating C Yes Solar Heat area 28 OOP Vario Exclusive Child Doll Motionsin NFZ D No Solar Heat 29 FFC Vario Exclusive Child Doll, BlanketMotions in safe seating E No Solar Heat area 30 RFS Joyride (new) BabyDoll Motions in entire F No Solar Heat seating area 31 ES None FabricCover Motions of track and (G) N.A. Solar Heat recline 32 FFA A32 LightClothes, Motions in safe seating H No Solar Heat Newspaper area 33 OOPA32 Light Clothes Motions in NFZ A Yes Solar Heat 34 FFC C33 LightClothes Motions in safe seating B No Solar Heat area 35 RFS Ultara BabyDoll, Blanket Motions in entire C No Solar Heat seating area 36 ES NoneBlanket Motions of track and (D) N.A. Solar Heat recline 37 FFA A22Medium Clothes Motions in safe seating E No Solar Heat area 38 OOP C21Medium Clothes Motions in NFZ F No Solar Heat 39 FFC C21 Medium ClothesMotions in safe seating G No Solar Heat area 40 RFS Arriva Baby Doll,Hood Motions in entire H No Solar Heat seating area 41 ES None HandbagMotions of track and (H) N.A. Ambient recline 42 FFA A11 Heavy Clothes,Motions in safe seating G Yes Ambient Magazine area 43 OOP A11 HeavyClothes Motions in NFZ F No Ambient 44 FFC Gerry Booster Infant DollMotions in safe seating E No Ambient area 45 RFS Fisher Price CS BabyDoll Motions in entire D No Ambient seating area 46 ES None BeadedCover, Motions of track and (C) N.A. Ambient Handbag recline 47 FFA A33Heavy Clothes Motions in safe seating B Yes Ambient area 48 OOP UltaraInflant Doll, Blanket Motions in NFZ A No Ambient 49 FFC Ultara InflantDoll, Blanket Motions in safe seating H No Ambient area 50 RFS Baby SafeBaby Doll, Motions in entire G No Ambient Handle up seating area 51 ESNone Fabric Cover, Motions of track and (F) N.A. Ambient Handbag recline52 FFA A21 Heavy Clothes, Motions in safe seating E No Ambient Newspaperarea 53 OOP A21 Heavy Clothes Motions in NFZ D Yes Ambient 54 FFC C12Heavy Clothes Motions in safe seating C No Ambient area 55 RFS VarioExclusive Baby Doll, Blanket Motions in entire B No Ambient seating area56 ES None Blanket, Handbag Motions of track and (A) N.A. Ambientrecline 57 FFA A12 Rain Clothes Motions in safe seating H No Ambientarea 58 OOP C23 Rain Clothes Motions in NFZ G No Ambient 59 FFC C23 RainClothes Motions in safe seating F No Ambient area 60 RFS Rock'n'RideBaby Doll Motions in entire E No Ambient seating area 61 ES None NoneMotions of track and (D) N.A. Air Conditioner recline 62 FFA A23 LightClothes, Motions in safe seating C Yes Air Conditioner Magazine area 63OOP A23 Light Clothes Motions in NFZ B No Air Conditioner 64 FFC CenturyInflant Doll Motions in safe seating A No Air Conditioner Ovation area65 RFS Century Baby Doll Motions in entire H No Air Conditioner Ovationseating area 66 ES None Beaded Cover Motions of track and (G) N.A. AirConditioner recline 67 FFA A32 Light Clothes Motions in safe seating FYes Air Conditioner area 68 OOP Fisher Price CS Child Doll Motions inNFZ E No Air Conditioner 69 FFC Fisher Price CS Child Doll, BlanketMotions in safe seating D No Air Conditioner area 70 RFS Gerry GuardBaby Doll Motions in entire C No Air Conditioner seating area 71 ES NoneFabric Cover Motions of track and (B) N.A. Air Conditioner recline 72FFA A22 Light Clothes, Motions in safe seating A No Air ConditionerNewspaper area 73 OOP A22 Light Clothes Motions in NFZ H Yes AirConditioner 74 FFC C32 Light Clothes Motions in safe seating G No AirConditioner area 75 RFS Smartmove ST Baby Doll, Blanket Motions inentire F No Air Conditioner seating area 76 ES None Blanket Motions oftrack and (E) N.A. Air Conditioner recline 77 FFA A11 Medium ClothesMotions in safe seating D No Air Conditioner area 78 OOP C22 MediumClothes Motions in NFZ C No Air Conditioner 79 FFC C22 Medium ClothesMotions in safe seating B No Air Conditioner area 80 RFS Discovery BabyDoll, Motions in entire A No Air Conditioner Handle up seating area 81ES None Pizza Box Motions of track and (B) N.A. Ambient recline 82 FFAA33 Rain Clothes, Motions in safe seating A Yes Ambient Magazine area 83OOP A33 Rain Clothes Motions in NFZ D Yes Ambient 84 FFC Champion InfantDoll Motions in safe seating C No Ambient area 85 RFS Champion Baby DollMotions in entire F No Ambient sealing area 86 ES None Beaded Cover,Motions of track and (E) N.A. Ambient Pizza Box recline 87 FFA A21 RainClothes Motions in safe seating H Yes Ambient area 88 OOP VarioExclusive Child Doll, Blanket Motions in NFZ G No Ambient 89 FFC VarioExclusive Child Doll, Blanket Motions in safe seating B No Ambient area90 RFS Joyride (new) Baby Doll, Hood Motions in entire A No Ambientseating area 91 ES None Fabric Cover, Motions of track and (D) N.A.Ambient Pizza Box recline 92 FFA A12 Rain Clothes, Motions in safeseating C No Ambient Newspaper area 93 OOP A12 Rain Clothes Motions inNFZ F No Ambient 94 FFC C23 Rain Clothes Motions in safe seating E NoAmbient area 95 RFS Ultara Baby Doll, Blanket Motions in entire H NoAmbient seating area 96 ES None Blanket, Pizza Box Motions of track and(G) N.A. Ambient recline 97 FFA A23 Light Clothes Motions in safeseating B No Ambient area 98 OOP C32 Light Clothes Motions in NFZ A NoAmbient 99 FFC C32 Light Clothes Motions in safe seating D No Ambientarea 100  RFS Arriva Baby Doll, Hood Motions in entire C No Ambientseating area 101  ES None None Motions of track and (F) N.A. Car Heatrecline 102  FFA A32 Light Clothes, Motions in safe seating E Yes CarHeat Magazine area 103  OOP A32 Light Clothes Motions in NFZ H Yes CarHeat 104  FFC Century 1000 Infant Doll Motions in safe seating G No CarHeat area 105  RFS Century 1000 Baby Doll Motions in entire B No CarHeat seating area 106  ES None Beaded Cover Motions of track and (A)N.A. Car Heat recline 107  FFA A22 Rain Clothes Motions in safe seatingD Yes Car Heat area 108  OOP Vario Exclusive Inflant Doll Motions in NFZC No Car Heat 109  FFC Touriva Inflant Doll, Blanket Motions in safeseating F No Car Heat area 110  RFS Century 590 Baby Doll Motions inentire E No Car Heat seating area 111  ES None Fabric Cover Motions oftrack and (H) N.A. Car Heat recline 112  FFA A11 Light Clothes, Motionsin safe seating G No Car Heat Newspaper area 113  OOP A11 Light ClothesMotions in NFZ B No Car Heat 114  FFC C32 Light Clothes Motions in safeseating A No Car Heat area 115  RFS Touriva Baby Doll, Blanket Motionsin entire D No Car Heat seating area 116  ES None Blanket Motions oftrack and (C) N.A. Car Heat recline 117  FFA A33 Heavy Clothes Motionsin safe seating F No Car Heat area 118  OOP C22 Heavy Clothes Motions inNFZ E No Car Heat 119  FFC C22 Heavy Clothes Motions in safe seating HNo Car Heat area 120  RFS TLC Baby Doll Motions in entire G No Car Heatseating area 121  ES None Attaché Case (flat) Motions of track and (G)N.A. Ambient recline 122  FFA A21 Heavy Clothes, Motions in safe seatingH Yes Ambient Magazine area 123  OOP A21 Heavy Clothes Motions in NFZ EYes Ambient 124  FFC Century Infant Doll Motions in safe seating F NoAmbient Ovation area 125  RFS Century Baby Doll Motions in entire C NoAmbient Ovation seating area 126  ES None Beaded Cover, Motions of trackand (D) N.A. Ambient Attaché Case recline 127  FFA A12 Rain ClothesMotions in safe seating A Yes Ambient area 128  OOP Fisher Price CSInflant Doll, Blanket Motions in NFZ B No Ambient 129  FFC Fisher PriceCS Inflant Doll Motions in safe seating G No Ambient area 130  RFS GerryGuard Baby Doll, Motions in entire H No Ambient Handle up seating area131  ES None Fabric Cover, Motions of track and (E) N.A. Ambient AttachéCase recline 132  FFA A23 Heavy Clothes, Motions in safe seating F NoAmbient Newspaper area 133  OOP A23 Heavy Clothes Motions in NFZ C NoAmbient 134  FFC C11 Heavy Clothes Motions in safe seating D No Ambientarea 135  RFS Smartmove 5T Baby Doll, Blanket Motions in entire A NoAmbient seating area 136  ES None Blanket, Motions of track and (B) N.A.Ambient Attaché Case recline 137  FFA A32 Rain Clothes Motions in safeseating G No Ambient area 138  OOP C33 Rain Clothes Motions in NFZ H NoAmbient 139  FFC C33 Rain Clothes Motions in safe seating E No Ambientarea 140  RFS Discovery Baby Doll, Motions in entire F No Ambient Handleup seating area 141  ES None Hand Bag Motions of track and (C) N.A.Solar Heat recline 142  FFA A22 Medium Clothes, Motions in safe seatingD Yes Solar Heat Magazine area 143  OOP A22 Heavy Clothes Motions in NFZA Yes Solar Heat 144  FFC Gerry Booster Child Doll Motions in safeseating B No Solar Heat area 145  RFS Fisher Price CS Baby Doll Motionsin entire G No Solar Heat seating area 146  ES None Beaded Cover,Motions of track and (H) N.A. Solar Heat Hand Bag recline 147  FFA A11Medium Clothes Motions in safe seating E Yes Solar Heat area 148  OOPVario Exclusive Inflant Doll Motions in NFZ F No Solar Heat 149  FFCUltara Inflant Doll, Blanket Motions in safe seating C No Solar Heatarea 150  RFS Baby Safe Baby Doll Motions in entire D No Solar Heatseating area 151  ES None Fabric Cover, Motions of track and (A) N.A.Solar Heat Hand Bag recline 152  FFA A33 Medium Clothes, Motions in safeseating B No Solar Heat Newspaper area 153  OOP A33 Medium ClothesMotions in NFZ G No Solar Heat 154  FFC C33 Medium Clothes Motions insafe seating H No Solar Heat area 155  RFS Vario Exclusive Baby Doll,Blanket Motions in entire E No Solar Heat seating area 156  ES NoneBlanket, Hand Bag Motions of track and (F) N.A. Solar Heat recline 157 FFA A21 Light Clothes Motions in safe seating C No Solar Heat area 158 OOP C21 Light Clothes Motions in NFZ D No Solar Heat 159  FFC C21 LightClothes Motions in safe seating A No Solar Heat area 160  RFSRock'n'Ride Baby Doll Motions in entire B No Solar Heat seating area

Network Independent Test Set Collection Matrix (Vehicle E) Rev 1.1(Under Construction)

[0262] # Class Subject/Object Attributes Actions Config. Belt Conditions 1 ES Motions of track and (A) N.A. Ambient recline  2 FFA Motions insafe sealing B Yes Ambient area  3 OOP Motions in NFZ C No Ambient  4FFC Motions in safe seating D No Ambient area  5 RFS Motions in entire ENo Ambient seating area  6 ES Motions of track and (F) N.A. Ambientrecline  7 FFA Motions in safe seating G Yes Ambient area  8 OOP Motionsin NFZ H No Ambient  9 FFC Motions in safe seating A No Ambient area 10RFS Motions in entire B No Ambient seating area 11 ES Motions of trackand (C) N.A. Ambient recline 12 FFA Motions in safe seating D No Ambientarea 13 OOP Motions in NFZ E Yes Ambient 14 FFC Motions in safe seatingF No Ambient area 15 RFS Motions in entire G No Ambient seating area 16ES Motions of track and (H) N.A. Ambient recline 17 FFA Motions in safeseating A No Ambient area 18 OOP Motions in NFZ B No Ambient (standing)19 FFC Motions in safe seating C No Ambient area 20 RFS Motions inentire D No Ambient seating area

Appendix 2 Analysis of Neural Network Training and Data PreprocessingMethods—An Example

[0263] 1. Introduction

[0264] The Artificial Neural Network that forms the “brains” of theOccupant Spatial Sensor needs to be trained to recognize airbag enableand disable patterns. The most important part of this training is thedata that is collected in the vehicle, which provides the patternscorresponding to these respective configurations. Manipulation of thisdata (such as filtering) is appropriate if this enhances the informationcontained in the data. Important too, are the basic network architectureand training methods applied, as these two determine the learning andgeneralization capabilities of the neural network. The ultimate test forall methods and filters is their effect on the network performanceagainst real world situations.

[0265] The Occupant Spatial Sensor (OSS) uses an artificial neuralnetwork (ANN) to recognize patterns that it has been trained to identifyas either airbag enable or airbag disable conditions. The pattern isobtained from four ultrasonic transducers that cover the front passengerseating area. This pattern consists of the ultrasonic echoes from theobjects in the passenger seat area. The signal from each of the fourtransducers consists of the electrical image of the return echoes, whichis processed by the electronics. The electronic processing comprisesamplification (logarithmic compression), rectification, and demodulation(band pass filtering), followed by discretization (sampling) anddigitization of the signal. The only software processing required,before this signal can be fed into the artificial neural network, isnormalization (i.e. mapping the input to numbers between 0 and 1).Although this is a fair amount of processing, the resulting signal isstill considered “raw”, because all information is treated equally.

[0266] It is possible to apply one or more software preprocessingfilters to the raw signal before it is fed into the artificial neuralnetwork. The purpose of such filters is to enhance the usefulinformation going into the ANN, in order to increase the systemperformance. This document describes several preprocessing filters thatwere applied to the ANN training of a particular vehicle.

[0267] 2. Data Description

[0268] The performance of the artificial neural network is dependent onthe data that is used to train the network. The amount of data and thedistribution of the data within the realm of possibilities are known tohave a large effect on the ability of the network to recognize patternsand to generalize. Data for the OSS is made up of vectors. Each vectoris a combination of the useful parts of the signals collected from fourultrasonic transducers. A typical vector could comprise on the order of100 data points, each representing the (time displaced) echo level asrecorded by the ultrasonic transducers.

[0269] Three different sets of data are collected. The first set, thetraining data, contains the patterns that the ANN is being trained on torecognize as either an airbag deploy or non-deploy scenario. The secondset is the independent test data. This set is used during the networktraining to direct the optimization of the network weights. The thirdset is the validation (or real world) data. This set is used to quantifythe success rate (or performance) of the finalized artificial neuralnetwork.

[0270] Table 1 shows the main characteristics of these three data sets,as collected for the vehicle. Three numbers characterize the sets. Thenumber of configurations characterizes how many different subjects andobjects were used. The number of setups is the product of the number ofconfigurations and the number of vehicle interior variations (seatposition and recline, roof and window state, etc.) performed for eachconfiguration. The total number of vectors is then made up of theproduct of the number of setups and the number of patterns collectedwhile the subject or object moves within the passenger volume. TABLE 1Characteristics of the Data Sets Data Set Configurations Setups VectorsTraining 130 1300 650,000 Independent Test 130 1300 195,000 Validation100  100  15,000

[0271] 1.1 Training Data Set Characteristics

[0272] The training data set can be split up in various ways intosubsets that show the distribution of the data. Table 2 shows thedistribution of the training set amongst three classes of passenger seatoccupancy: Empty Seat, Human Occupant, and Child Seat. All humanoccupants were adults of various sizes. No children were part of thetraining data set other then those seated in Forward Facing Child Seats.Table 3 shows a further breakup of the Child Seats into Forward FacingChild Seats, Rearward Facing Child Seats, Rearward Facing Infant Seats,and out-of-position Forward Facing Child Seats. Table 4 shows adifferent type of distribution; one based on the environmentalconditions inside the vehicle. TABLE 2 Distribution of Main TrainingSubjects Occupancy Representation Empty Seat 10% Human Occupant 32%Child Seat 58%

[0273] TABLE 3 Child Seat Distribution Child Seat ConfigurationRepresentation Foward Facing Child Seat 40% Forward Facing Child SeatOut-of-Position  4% Rearward Facing Child Seat 27% Rearward FacingInfant Seat 29%

[0274] TABLE 4 Distribution of Environmental Conditions EnvironmentalCondition Representation Ambient 56% Static Heat (Solar Lamp) 25%Dynamic Heat (Car Heat) 13% Dynamic Cooling (Car A C)  6%

[0275] 1.2 Independent Test Data Characteristics

[0276] The independent test data is created using the sameconfigurations, subjects, objects, and conditions as used for thetraining data set. Its makeup and distributions are therefore the sameas those of the training data set.

[0277] 1.3 Validation Data Characteristics

[0278] The distribution of the validation data set into its main subsetsis shown in Table 5. This distribution is close to that of the trainingdata set. However, the human occupants comprised both children (12% oftotal) as well as adults (27% of total). Table 6 shows the distributionof human subjects. Contrary to the training and independent test datasets, data was collected on children ages 3 and 6 that were not seatedin a child restraint of any kind. Table 7 shows the distribution of thechild seats used. On the other hand, no data was collected on ForwardFacing Child Seats that were out-of-position. The child and infant seatsused in this data set are different from those used in the training andindependent test data sets. The validation data was collected withvarying environmental conditions as shown in Table 8. TABLE 5 ValidationData Distribution Occupancy Representation Empty Seat  8% Human Occupant39% Child Seat 53%

[0279] TABLE 6 Human Subject Distribution Normally Human OccupantRepresentation Seated Out-of-Position Child age 3 15% 50% 50% Child age6 15% 50% 50% Adult 5^(th) percentile Female 23% 67% 33% Adult 50^(th)percentile Male 23% 67% 33% Adult 95^(th) percentile Male 23% 67% 33%

[0280] TABLE 7 Child Seat Distribution Child Seat ConfigurationRepresentation Forward Facing Child Seat 11% Forward Facing Booster Seat11% Rearward Facing Child Seat 38% Rearward Facing Infant Seat 40%

[0281] TABLE 8 Distribution of Environmental Conditions EnvironmentalCondition Representation Ambient 63% Static Heat (Solar Lamp) 13%Dynamic Heat (Car Heat) 12% Dynamic Cooling (Car Air Conditioner) 12%

[0282] 3. Network Training

[0283] The baseline network consisted of a four layer back-propagationnetwork with 117 input layer nodes, 20 and 7 nodes respectively in thetwo hidden layers, and 1 output layer node. The input layer is made upof inputs from four ultrasonic transducers. These were located in thevehicle on the rear quarter panel (A), the A-pillar (B), and theover-head console (C, H). Table 9 shows the number of points, taken fromeach of these channels that make up one vector. TABLE 9 TransducerVolume Starting Point End Point Transducer Sample Time (ms) Distance(mm) Sample Time (ms) Distance (mm) A 5 0.83 142 29 4.84 822 B 3 0.50 8535 5.84 992 C 7 1.17 198 34 5.67 964 H 2 0.33 57 32 5.34 907

[0284] The artificial neural network is implemented using theNeuralWorks Professional II/Plus software. The method used for trainingthe decision mathematical model was back-propagation with ExtendedDelta-Bar-Delta learning rule and sigmoid transfer function. TheExtended DBD paradigm uses past values of the gradient to infer thelocal curvature of the error surface. This leads to a learning rule inwhich every connection has a different learning rate and a differentmomentum term, both of which are automatically calculated.

[0285] The network was trained using the above-described training andindependent test data sets. An optimum (against the independent testset) was found after 3,675,000 training cycles. Each training cycle uses30 vectors (known as the epoch), randomly chosen from the 650,000available training set vectors. Table 10 shows the performance of thebaseline network. TABLE 10 Baseline Network Performance Self TestSuccess Rate 95.3% Independent Test Success Rate 94.5% Validation TestSuccess Rate 92.7%

[0286] The network performance has been further analyzed byinvestigating the success rates against subsets of the independent testset. The success rate against the airbag enable conditions at 94.6% isvirtually equal to that against the airbag disable conditions at 94.4%.Table 11 shows the success rates for the various occupancy subsets.Table 12 shows the success rates for the environmental conditionssubsets. Although the distribution of this data was not entirelybalanced throughout the matrix, it can be concluded that the systemperformance is not significantly degraded by heat sources. TABLE 11Performance per Occupancy Subset Occupancy Independent Test Empty Seat96.1% Normally Seated Adult 92.1% Rearward Facing Child/Infant Seat94.1% Forward Facing Child Seat 96.9% Out-of-Position Human/FFCS 93.0%

[0287] TABLE 12 Performance per Environmental Conditions SubsetEnvironmental Condition Independent Test Ambient 95.4% Long Term Heat(Lamp Heat) 95.2% Sort Term Heating/Cooling (HVAC) 93.5%

[0288] 3.1 Normalization

[0289] Normalization is used to scale the real world data range into arange acceptable for the network training. The NeuralWorks softwarerequires the use of a scaling factor to bring the input data into arange of 0 to 1, inclusive. Several normalization methods have beenexplored for their effect on the system performance.

[0290] The real world data consists of 12 bit, digitized signals withvalues between 0 and 4095. Chart 1 shows a typical raw signal. A rawvector consists of combined sections of four signals.

Chart 1 Sample Raw Input Signal

[0291] Three methods of normalization of the individual vectors havebeen investigated:

[0292] a. Normalization using the highest and lowest value of the entirevector (baseline).

[0293] b. Normalization of the transducer channels that make up thevector, individually. This method uses the highest and lowest values ofeach channel.

[0294] c. Normalization with a fixed range ([0,4095]).

[0295] The results of the normalization study are summarized in Table13. TABLE 13 Normalization Study Results Normalization Method Self TestIndependent Test Validation Test a. Whole Vector (base) 95.3% 94.5%92.7% b. Per Channel 94.9% 93.8% 90.3% c. Fixed Range [0,4095] 95.6%90.3% 88.3%

[0296] A higher performance results from normalizing across the entirevector versus normalizing per channel. This can be explained from thefact that the baseline method retains the information contained in therelative strength of the signal from one transducer compared to another.This information is lost when using the second method.

[0297] Normalization using a fixed range retains the informationcontained in the relative strength of one vector compared to the next.From this it could be expected that the performance of the networktrained with fixed range normalization would increase over that of thebaseline method. However, without normalization, the input range is, asa rule, not from zero to the maximum value (see FIG. 1). The absolutevalue of the data at the input layer affects the network weightadjustment (see equations [1] and [2]). During network training, vectorswith a smaller input range will affect the weights calculated for eachprocessing element (neuron) differently than vectors that do span thefull range.

Δw _(ij) ^([s]) =lcoef·e _(j) ^([s]) ·x _(I) ^([s−1])  [1]

e _(j) ^([s]) =x _(j) ^([s]·()1.0−x _(j) ^([s])·Δ) _(k)(e _(k) ^([s+1])·w _(kj) ^([s+l]))  [2]

[0298] Δw_(ij) ^([s]) is the change in the network weights; lcoef is thelearning coefficient; e_(j) ^([s]) is the local error at neuron j inlayer s; x_(I) ^([s]) is the current output state of neuron j in layers.

[0299] Variations in the highest and lowest values in the input layer,therefore, have a negative effect on the training of the network. Thisis reflected in a lower performance against the validation data set.

[0300] A secondary effect of normalization is that it increases theresolution of the signal by stretching it out over the full range of 0to 1, inclusive. As the network predominantly learns from higher peaksin the signal, this results in better generalization capabilities andtherefore in a higher performance.

[0301] It must be concluded that the effects of the fixed range of inputvalues and the increased resolution resulting from the baselinenormalization method have a stronger effect on the network training thanretaining the information contained in the relative vector strength.

[0302] 3.2 Low Threshold Filters

[0303] Not all information contained in the raw signals can beconsidered useful for network training. Low amplitude echoes arereceived back from objects on the outskirts of the ultrasonic field thatshould not be included in the training data. Moreover, low amplitudenoise, from various sources, is contained within the signal. This noiseshows up strongest where the signal is weak. By using a low thresholdfilter, the signal to noise ratio of the vectors can be improved beforethey are used for network training.

[0304] Three cutoff levels were used: 5%, 10%, and 20% of the signalmaximum value (4095). The method used, brings the values below thethreshold up to the threshold level. Subsequent vector normalization(baseline method) stretches the signal to the full range of [0,1].

[0305] The results of the low threshold filter study are summarized inTable 14. TABLE 14 Low Threshold Filter Study Results Threshold LevelSelf Test Independent Test Validation Test none (base) 95.3% 94.5% 92.7%5% of 4095 95.3% 94.4% 91.9% 10% of 4095 95.3% 94.3% 92.5% 20% of 409595.1% 94.2% 86.4%

[0306] The performance of the networks trained with 5% and 10% thresholdfilter is similar to that of the baseline network. A small performancedegradation is observed for the network trained with a 20% thresholdfilter. From this it is concluded that the noise level is sufficientlylow to not affect the network training. At the same time it can beconcluded that the lower 10% of the signal can be discarded withoutaffecting the network performance. This allows the definition ofdemarcation lines on the outskirts of the ultrasonic field where thesignal is equal to 10% of the maximum field strength.

[0307] 4. Network Types

[0308] The baseline network is a back-propagation type network.Back-propagation is a general-purpose network paradigm that has beensuccessfully used for prediction, classification, system modeling, andfiltering as well as many other general types of problems. Backpropagation learns by calculating an error between desired and actualoutput and propagating this error information back to each node in thenetwork. This back-propagated error is used to drive the learning ateach node. Some of the advantages of a back-propagation network are thatit attempts to minimize the global error and that it can provide a verycompact distributed representation of complex data sets. Some of thedisadvantages are its slow learning and the irregular boundaries andunexpected classification regions due to the distributed nature of thenetwork and the use of a transfer functions that is unbounded. Some ofthese disadvantages can be overcome by using a modified back-propagationmethod such as the Extended Delta-Bar-Delta paradigm. The EDBD algorithmautomatically calculates the learning rate and momentum for eachconnection in the network, which facilitates optimization of the networktraining.

[0309] Many other network architectures exist that have differentcharacteristics than the baseline network. One of these is the LogiconProjection Network. This type of network combines the advantages ofclosed boundary networks with those of open boundary networks (to whichthe back-propagation network belongs). Closed boundary networks are fastlearning because they can immediately place prototypes at the input datapoints and match all input data to these prototypes. Open boundarynetworks, on the other hand, have the capability to minimize the outputerror through gradient decent.

[0310] 5. Conclusions

[0311] The baseline artificial neural network trained to a success rateof 92.7% against the validation data set. This network has a four-layerback-propagation architecture and uses the Extended Delta-Bar-Deltalearning rule and sigmoid transfer function. Pre-processing comprisedvector normalization while post-processing comprised a “five consistentdecision” filter.

[0312] The objects and subjects used for the independent test data werethe same as those used for the training data. This may have negativelyaffected the network's classification generalization abilities.

[0313] The spatial distribution of the independent test data was as wideas that of the training data. This has resulted in a network that cangeneralize across a large spatial volume. A higher performance across asmaller volume, located immediately around the peak of the normaldistribution, combined with a lower performance on the outskirts of thedistribution curve, might be preferable.

[0314] To achieve this, the distribution of the independent test setneeds to be a reflection of the normal distribution for the system(a.k.a. native population).

[0315] Modifying the pre-processing method or applying additionalpre-processing methods did not show a significant improvement of theperformance over that of the baseline network. The baselinenormalization method gave the best results as it improves the learningby keeping the input values in a fixed range and increases the signalresolution. The lower threshold study showed that the network learnsfrom the larger peaks in the echo pattern. Pre-processing techniquesshould be aimed at increasing the signal resolution to bring out thesepeaks.

[0316] A further study could be performed to investigate combining alower threshold with fixed range normalization, using a range less thanfull scale. This would force each vector to include at least one pointat the lower threshold value and one value in saturation, effectivelyforcing each vector into a fixed range that can be mapped between 0 and1, inclusive. This would have the positive effects associated with thebaseline normalization, while retaining the information contained in therelative vector strength. Raw vectors points that, as a result of thescaling, would fall outside the range of 0 to 1 would then be mapped to0 and 1 respectively.

[0317] Post-processing should be used to enhance the network recognitionability with a memory function. The possibilities for such are currentlyfrustrated by the necessity of one network performing both objectclassification as well as spatial locating functions. Performing thespatial locating function requires flexibility to rapidly update thesystem status. Object classification, on the other hand, benefits fromdecision rigidity to nullify the effect of an occasional pattern that isincorrectly classified by the network.

Appendix 3 Process for Training an OPS System DOOP Network for aSpecific Vehicle

[0318] 1. Define customer requirements and deliverables

[0319] 1.1. Number of zones

[0320] 1.2. Number of outputs

[0321] 1.3. At risk zone definition

[0322] 1.4. Decision definition i.e. empty seat at risk, safe seating,or not critical and undetermined

[0323] 1.5. Determine speed of DOOP decision

[0324] 2. Develop PERT chart for the program

[0325] 3. Determine viable locations for the transducer mounts

[0326] 3.1. Manufacturability

[0327] 3.2. Repeatability

[0328] 3.3. Exposure (not able to damage during vehicle life)

[0329] 4. Evaluate location of mount logistics

[0330] 4.1. Field dimensions

[0331] 4.2. Multipath reflections

[0332] 4.3. Transducer Aim

[0333] 4.4. Obstructions/Unwanted data

[0334] 4.5. Objective of view

[0335] 4.6. Primary DOOP transducers requirements

[0336] 5. Develop documentation logs for the program (vehicle books)

[0337] 6. Determine vehicle training variables

[0338] 6.1. Seat track stops

[0339] 6.2. Steering wheel stops

[0340] 6.3. Seat back angles

[0341] 6.4. DOOP transducer blockage during crash

[0342] 6.5. Etc . . .

[0343] 7. Determine and mark at risk zone in vehicle

[0344] 8. Evaluate location physical impediments

[0345] 8.1. Room to mount/hide transducers

[0346] 8.2. Sufficient hard mounting surfaces

[0347] 8.3. Obstructions

[0348] 9. Develop matrix for training, independent, validation, and DOOPdata sets

[0349] 10. Determine necessary equipment needed for data collection

[0350] 10.1. Child/booster/infant seats

[0351] 10.2. Maps/razors/makeup

[0352] 10.3. Etc . . .

[0353] 11. Schedule sled tests for initial and final DOOP networks

[0354] 12. Design test buck for DOOP

[0355] 13. Design test dummy for DOOP testing

[0356] 14. Purchase any necessary variables

[0357] 14.1. Child/booster/infant seats

[0358] 14.2. Maps/razors/makeup

[0359] 14.3. Etc . . .

[0360] 15. Develop automated controls of vehicle accessories

[0361] 15.1. Automatic seat control for variable empty seat

[0362] 15.2. Automatic seat back angle control for variable empty seat

[0363] 15.3. Automatic window control for variable empty seat

[0364] 15.4. Etc . . .

[0365] 16. Acquire equipment to build automated controls

[0366] 17. Build & install automated controls of vehicle variables

[0367] 18. Install data collection aides

[0368] 18.1. Thermometers

[0369] 18.2. Seat track gauge

[0370] 18.3. Seat angle gauge

[0371] 18.4. Etc . . .

[0372] 19. Install switched and fused wiring for:

[0373] 19.1. Transducer pairs

[0374] 19.2. Lasers

[0375] 19.3. Decision Indicator Lights

[0376] 19.4. System box

[0377] 19.5. Monitor

[0378] 19.6. Power automated control items

[0379] 19.7. Thermometers, potentiometers

[0380] 19.8. DOOP occupant ranging device

[0381] 19.9. DOOP ranging indicator

[0382] 19.10. Etc . . .

[0383] 20. Write DOOP operating software for OPS system box

[0384] 21. Validate DOOP operating software for OPS

[0385] 22. Build OPS system control box for the vehicle with specialDOOP operating software

[0386] 23. Validate & document system control box

[0387] 24. Write vehicle specific DOOP data collection software(pollbin)

[0388] 25. Write vehicle specific DOOP data evaluation program(picgraph)

[0389] 26. Evaluate DOOP data collection software

[0390] 27. Evaluate DOOP data evaluation software

[0391] 28. Load DOOP data collection software on OPS system box andvalidate

[0392] 29. Load DOOP data evaluation software on OPS system box andvalidate

[0393] 30. Train technicians on DOOP data collection techniques and useof data collection software

[0394] 31. Design prototype mounts based on known transducer variables

[0395] 32. Prototype mounts

[0396] 33. Pre-build mounts

[0397] 33.1. Install transducers in mounts

[0398] 33.2. Optimize to eliminate crosstalk

[0399] 33.3. Obtain desired field

[0400] 33.4. Validate performance of DOOP requirements for mounts

[0401] 34. Document mounts

[0402] 34.1. Polar plots of fields

[0403] 34.2. Drawings with all mount dimensions

[0404] 34.3. Drawings of transducer location in the mount

[0405] 35. Install mounts in the vehicle

[0406] 36. Map fields in the vehicle using ATI designed apparatus andspecification

[0407] 37. Map performance in the vehicle of the DOOP transducerassembly

[0408] 38. Determine sensor volume

[0409] 39. Document vehicle mounted transducers and fields

[0410] 39.1. Mapping per ATI specification

[0411] 39.2. Photographs of all fields

[0412] 39.3. Drawing and dimensions of installed mounts

[0413] 39.4. Document sensor volume

[0414] 39.5. Drawing and dimensions of aim & field

[0415] 40. Using data collection software and OPS system box collectinitial 16 sheets of training, independent, and validation data

[0416] 41. Determine initial conditions for training the ANN

[0417] 41.1. Normalization method

[0418] 41.2. Training via back propagation or ?

[0419] 41.3. Weights

[0420] 41.4. Etc . . .

[0421] 42. Pre-process data

[0422] 43. Train an ANN on above data

[0423] 44. Develop post processing strategy if necessary

[0424] 45. Develop post processing software

[0425] 46. Evaluate ANN with validation data and in vehicle analysis

[0426] 47. Perform sled tests to confirm initial DOOP results

[0427] 48. Document DOOP testing results and performance

[0428] 49. Rework mounts and repeat steps 31 through 48 if necessary

[0429] 50. Meet with customer and review program

[0430] 51. Develop strategy for customer directed outputs

[0431] 51.1. Develop strategy for final ANN multiple decision networksif necessary

[0432] 51.2. Develop strategy for final ANN multiple layer networks ifnecessary

[0433] 51.3. Develop strategy for DOOP layer/network

[0434] 52. Design daily calibration jig

[0435] 53. Build daily calibration jig

[0436] 54. Develop daily calibration test

[0437] 55. Document daily calibration test procedure & jig

[0438] 56. Collect daily calibration tests

[0439] 57. Document daily calibration test results

[0440] 58. Rework vehicle data collection markings for customer directedoutputs

[0441] 58.1. Multiple zone identifiers for data collection

[0442] 59. Schedule subjects for all data sets

[0443] 60. Train subjects for data collection procedures

[0444] 61. Using DOOP data collection software and OPS system boxcollect initial 16 sheets of training, independent, and validation data

[0445] 62. Collect total amount of vectors deemed necessary by programdirectives, amount will vary as outputs and complexity of ANN varies

[0446] 63. Determine initial conditions for training the ANN

[0447] 63.1. Normalization method

[0448] 63.2. Training via back propagation or ?

[0449] 63.3. Weights

[0450] 63.4. Etc . . .

[0451] 64. Pre-process data

[0452] 65. Train an ANN on above data

[0453] 66. Develop post processing strategy

[0454] 66.1. Weighting

[0455] 66.2. Averaging

[0456] 66.3. Etc . . .

[0457] 67. Develop post processing software

[0458] 68. Evaluate ANN with validation data

[0459] 69. Perform in vehicle hole searching and analysis

[0460] 70. Perform in vehicle non sled mounted DOOP tests

[0461] 71. Determines need for further training or processing

[0462] 72. Repeat steps 58 through 71 if necessary

[0463] 73. Perform sled tests to confirm initial DOOP results

[0464] 74. Document DOOP testing results and performance

[0465] 75. Repeat steps 58 through 74 if necessary

[0466] 76. Write summary performance report

[0467] 77. Presentation of vehicle to the customer

[0468] 78. Delivered an OPS equipped vehicle to the customer

We claim:
 1. An arrangement for detecting the presence of an object in apassenger compartment of a vehicle, comprising a plurality of ultrasonictransmitters for transmitting ultrasonic waves into the passengercompartment, each of said transmitters operating at a distincttransmitting frequency and positioned at a distinct location relative tothe other of said transmitters, at least one receiver disposed so as toreceive from the passenger compartment ultrasonic waves transmitted fromat least one of said transmitters and modified by passing through atleast part of the passenger compartment, and a processor operativelycoupled to said at least one receiver for determining whether an objectis located in the passenger compartment based on the ultrasonic wavesreceived by said at least one receiver.
 2. The arrangement of claim 1,wherein said at least one receiver and one of said transmitters form atransducer.
 3. The arrangement of claim 1, wherein said at least onereceiver comprises a plurality of receivers, said receivers and saidtransmitters being arranged to form transducers including one of saidreceivers and one of said transmitters.
 4. The arrangement of claim 1,wherein a first one of said transmitters is arranged on a ceiling of thevehicle and a second one of said transmitters is arranged at a differentlocation in the vehicle such that a first axis connecting the first andsecond transmitters is substantially parallel to a second axistraversing a volume in the passenger compartment of the vehicle above aseat in which the object is situated.
 5. The arrangement of claim 1,wherein said processor controls deployment of an occupant restraintdevice based on the ultrasonic waves received by said at least onereceiver.
 6. The arrangement of claim 1, wherein one of saidtransmitters is arranged on a dashboard of the vehicle.
 7. Thearrangement of claim 1, wherein said plurality of transmitters comprisesthree transmitters, a first one of said transmitters being arranged on aceiling of the vehicle, a second one of said transmitters being arrangedon a dashboard of the vehicle and a third one of said transmitters beingarranged on or adjacent an interior side surface of the passengercompartment.
 8. The arrangement of claim 1, wherein said plurality oftransmitters comprises four transmitters, a first one of saidtransmitters being arranged on a ceiling of the vehicle, a second one ofsaid transmitters being arranged on a dashboard of the vehicle, a thirdone of said transmitters being arranged on an interior side surface ofthe passenger compartment and a fourth one of said transmitters beingarranged on or adjacent an interior side surface of the passengercompartment.
 9. The arrangement of claim 1, wherein said processoremploys pattern recognition techniques to determine whether an object islocated in the passenger compartment.
 10. An arrangement for controllingactivation of a deployable safety restraint system of a vehicle,comprising a plurality of ultrasonic transmitters for transmittingultrasonic waves into a passenger compartment of the vehicle, each ofsaid transmitters operating at a distinct transmitting frequency andpositioned at a distinct location relative to the other of saidtransmitters, at least one receiver disposed so as to receive from thepassenger compartment ultrasonic waves transmitted from at least one ofsaid transmitters and modified by passing through at least part of thepassenger compartment, and a processor operatively coupled to said atleast one receiver for controlling deployment of the safety restraintdevice based on the ultrasonic waves received by said at least onereceiver.
 11. The arrangement of claim 10, wherein said at least onereceiver and one of said transmitters form a transducer.
 12. Thearrangement of claim 10, wherein said at least one receiver comprises aplurality of receivers, said receivers and said transmitters beingarranged to form transducers including one of said receivers and one ofsaid transmitters.
 13. The arrangement of claim 10, wherein a first oneof said transmitters is arranged on a ceiling of the vehicle and asecond one of said transmitters is arranged at a different location inthe vehicle such that a first axis connecting the first and secondtransmitters is substantially parallel to a second axis traversing avolume in the passenger compartment of the vehicle above a seat in whichan object is situated.
 14. The arrangement of claim 10, wherein saidprocessor determines whether an object is located in the passengercompartment based on the ultrasonic waves received by said at least onereceiver.
 15. The arrangement of claim 10, wherein one of saidtransmitters is arranged on a dashboard of the vehicle.
 16. Thearrangement of claim 10, wherein said plurality of transmitterscomprises three transmitters, a first one of said transmitters beingarranged on a ceiling of the vehicle, a second one of said transmittersbeing arranged on a dashboard of the vehicle and a third one of saidtransmitters being arranged on or adjacent an interior side surface ofthe passenger compartment.
 17. The arrangement of claim 10, wherein saidplurality of transmitters comprises four transmitters, a first one ofsaid transmitters being arranged on a ceiling of the vehicle, a secondone of said transmitters being arranged on a dashboard of the vehicle, athird one of said transmitters being arranged on an interior sidesurface of the passenger compartment and a fourth one of saidtransmitters being arranged on or adjacent an interior side surface ofthe passenger compartment.
 18. The arrangement of claim 10, wherein saidprocessor employs pattern recognition techniques to analyze the wavesreceived by said at least one receiver.
 19. An arrangement fordetermining the location of an object in a passenger compartment of avehicle, comprising a plurality of ultrasonic transmitters fortransmitting ultrasonic waves into the passenger compartment, each ofsaid transmitters operating at a distinct transmitting frequency andpositioned at a distinct location relative to the other of saidtransmitters, at least one receiver disposed so as to receive from thepassenger compartment ultrasonic waves transmitted from at least one ofsaid transmitters and modified by passing through at least part of thepassenger compartment, and a processor operatively coupled to said atleast one receiver for determining the location of the object in thepassenger compartment based on the ultrasonic waves received by said atleast one receiver.
 20. The arrangement of claim 19, wherein said atleast one receiver and one of said transmitters form a transducer. 21.The arrangement of claim 19, wherein said at least one receivercomprises a plurality of receivers.
 22. The arrangement of claim 21,wherein said receivers and said transmitters are arranged to formtransducers including one of said receivers and one of said transmitterswhereby the distance between the object and each of said receivers isobtained based on the ultrasonic waves received by said receiver suchthat the location of the object can be determined from the distancesbetween said receivers and the object.
 23. The arrangement of claim 19,wherein a first one of said transmitters is arranged on a ceiling of thevehicle and a second one of said transmitters is arranged at a differentlocation in the vehicle such that a first axis connecting the first andsecond transmitters is substantially parallel to a second axistraversing a volume in the passenger compartment of the vehicle above aseat in which the object is situated.
 24. The arrangement of claim 19,wherein said processor controls deployment of an occupant restraintdevice based on the location of the object.
 25. The arrangement of claim19, wherein one of said transmitters is arranged on a dashboard of thevehicle.
 26. The arrangement of claim 19, wherein said plurality oftransmitters comprises three transmitters, a first one of saidtransmitters being arranged on a ceiling of the vehicle, a second one ofsaid transmitters being arranged on a dashboard of the vehicle and athird one of said transmitters being arranged on or adjacent an interiorside surface of the passenger compartment.
 27. The arrangement of claim19, wherein said plurality of transmitters comprises four transmitters,a first one of said transmitters being arranged on a ceiling of thevehicle, a second one of said transmitters being arranged on a dashboardof the vehicle, a third one of said transmitters being arranged on aninterior side surface of the passenger compartment and a fourth one ofsaid transmitters being arranged on or adjacent an interior side surfaceof said passenger compartment.
 28. The arrangement of claim 19, whereinsaid processor employs pattern recognition techniques to determine thelocation of the object.
 29. An arrangement for detecting the presence ofan object in a passenger compartment of a vehicle, comprising a firstreceiver arranged on a ceiling of the vehicle, a second receiverarranged at a different location in the vehicle than said first receiversuch that a first axis connecting said first and second receivers issubstantially parallel to a second axis traversing a volume in thepassenger compartment of the vehicle above a seat in which the object issituated, a third receiver arranged at a different location in thepassenger compartment than the first and second receivers, each of saidfirst, second and third receivers comprising distance measurement meanssuch that a first distance from said first receiver to the object isobtained based on the output of said first receiver; a second distancefrom said second receiver to the object is obtained based on the outputof said second receiver and a third distance from said third receiver tothe object is obtained based on the output of said third receiver, and aprocessor for analyzing said first, second and third distances anddetermining if an object is present based thereon.
 30. The arrangementof claim 29, wherein said first, second and third receivers are arrangedto receive ultrasonic radiation.
 31. The arrangement of claim 29,wherein said first, second and third receivers are arranged to receiveelectromagnetic radiation.
 32. The arrangement of claim 29, furthercomprising: a fourth receiver arranged at a different location in thepassenger compartment than said first, second and third receivers, saidfourth receiver comprising distance measurement means such that a fourthdistance from said fourth receiver to the object based on the output ofsaid fourth receiver, said processor analyzing said first, second, thirdand fourth distances and determining if an object is present basedthereon.
 33. The arrangement of claim 29, wherein said first, second andthird receivers are of the same type.
 34. The arrangement of claim 29,further comprising at least one transmitter for transmitting waves intothe passenger compartment, said first, second and third receivers beingarranged to receive the waves transmitted by said at least onetransmitter.
 35. An arrangement for controlling activation of adeployable safety restraint system of a vehicle, comprising a firstreceiver arranged on a ceiling of the vehicle, a second receiverarranged at a different location in the vehicle than said first receiversuch that a first axis connecting said first and second receivers issubstantially parallel to a second axis traversing a volume in apassenger compartment of the vehicle above a seat in which an object issituated, a third receiver arranged at a different location in thepassenger compartment than the first and second receivers, each of saidfirst, second and third receivers comprising distance measurement meanssuch that a first distance from said first receiver to the object isobtained based on the output of said first receiver; a second distancefrom said second receiver to the object is obtained based on the outputof said second receiver and a third distance from said third receiver tothe object is obtained based on the output of said third receiver, and aprocessor for analyzing said first, second and third distances andcontrolling activation of the safety restraint device based thereon. 36.The arrangement of claim 35, wherein said first, second and thirdreceivers are arranged to receive ultrasonic radiation.
 37. Thearrangement of claim 35, wherein said first, second and third receiversare arranged to receive electromagnetic radiation.
 38. The arrangementof claim 35, further comprising: a fourth receiver arranged at adifferent location in the passenger compartment than said first, secondand third receivers, said fourth receiver comprising distancemeasurement means such that a fourth distance from said fourth receiverto the object based on the output of said fourth receiver, saidprocessor analyzing said first, second, third and fourth distances andcontrolling activation of the safety restraint device based thereon. 39.The arrangement of claim 35, wherein said first, second and thirdreceivers are of the same type.
 40. The arrangement of claim 35, furthercomprising at least one transmitter for transmitting waves into thepassenger compartment, said first, second and third receivers beingarranged to receive the waves transmitted by said at least onetransmitter.
 41. An arrangement for determining the location of anobject in a passenger compartment of a vehicle, comprising a firstreceiver arranged on a ceiling of the vehicle, a second receiverarranged at a different location in the vehicle than said first receiversuch that a first axis connecting said first and second receivers issubstantially parallel to a second axis traversing a volume in thepassenger compartment of the vehicle above a seat in which the object issituated, a third receiver arranged at a different location in thepassenger compartment than the first and second receivers, each of saidfirst, second and third receivers comprising distance measurement meanssuch that a first distance from said first receiver to the object isobtained based on the output of said first receiver; a second distancefrom said second receiver to the object is obtained based on the outputof said second receiver and a third distance from said third receiver tothe object is obtained based on the output of said third receiver, and aprocessor for analyzing said first, second and third distances anddetermining the location of the object based thereon.
 42. Thearrangement of claim 41, wherein said first, second and third receiversare arranged to receive ultrasonic radiation.
 43. The arrangement ofclaim 41, wherein said first, second and third receivers are arranged toreceive electromagnetic radiation.
 44. The arrangement of claim 41,further comprising: a fourth receiver arranged at a different locationin the passenger compartment than said first, second and thirdreceivers, said fourth receiver comprising distance measurement meanssuch that a fourth distance from said fourth receiver to the objectbased on the output of said fourth receiver, said processor analyzingsaid first, second, third and fourth distances and determining thelocation of the object based thereon.
 45. The arrangement of claim 41,wherein said first, second and third receivers are of the same type. 46.The arrangement of claim 41, further comprising at least one transmitterfor transmitting waves into the passenger compartment, said first,second and third receivers being arranged to receive the wavestransmitted by said at least one transmitter.